Overview

Brought to you by YData

Dataset statistics

Number of variables88
Number of observations30009
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.1 MiB
Average record size in memory704.0 B

Variable types

Numeric10
Categorical78

Alerts

city_Babahoyo is highly overall correlated with state_Los RiosHigh correlation
city_Cuenca is highly overall correlated with clusterHigh correlation
city_El Carmen is highly overall correlated with state_ManabiHigh correlation
city_Esmeraldas is highly overall correlated with state_EsmeraldasHigh correlation
city_Guaranda is highly overall correlated with state_BolivarHigh correlation
city_Guayaquil is highly overall correlated with state_GuayasHigh correlation
city_Ibarra is highly overall correlated with state_ImbaburaHigh correlation
city_Latacunga is highly overall correlated with state_CotopaxiHigh correlation
city_Loja is highly overall correlated with state_LojaHigh correlation
city_Machala is highly overall correlated with state_El OroHigh correlation
city_Manta is highly overall correlated with state_ManabiHigh correlation
city_Puyo is highly overall correlated with state_PastazaHigh correlation
city_Quevedo is highly overall correlated with state_Los RiosHigh correlation
city_Quito is highly overall correlated with cluster and 1 other fieldsHigh correlation
city_Riobamba is highly overall correlated with state_ChimborazoHigh correlation
city_Salinas is highly overall correlated with state_Santa ElenaHigh correlation
city_Santo Domingo is highly overall correlated with state_Santo Domingo de los TsachilasHigh correlation
cluster is highly overall correlated with city_Cuenca and 7 other fieldsHigh correlation
days_after_earthquake is highly overall correlated with id and 1 other fieldsHigh correlation
dcoilwtico is highly overall correlated with id and 1 other fieldsHigh correlation
family_GROCERY I is highly overall correlated with salesHigh correlation
id is highly overall correlated with days_after_earthquake and 2 other fieldsHigh correlation
onpromotion is highly overall correlated with salesHigh correlation
sales is highly overall correlated with family_GROCERY I and 1 other fieldsHigh correlation
state_Bolivar is highly overall correlated with city_GuarandaHigh correlation
state_Chimborazo is highly overall correlated with city_RiobambaHigh correlation
state_Cotopaxi is highly overall correlated with city_LatacungaHigh correlation
state_El Oro is highly overall correlated with city_MachalaHigh correlation
state_Esmeraldas is highly overall correlated with city_EsmeraldasHigh correlation
state_Guayas is highly overall correlated with city_Guayaquil and 1 other fieldsHigh correlation
state_Imbabura is highly overall correlated with city_IbarraHigh correlation
state_Loja is highly overall correlated with city_LojaHigh correlation
state_Los Rios is highly overall correlated with city_Babahoyo and 1 other fieldsHigh correlation
state_Manabi is highly overall correlated with city_El Carmen and 1 other fieldsHigh correlation
state_Pastaza is highly overall correlated with city_PuyoHigh correlation
state_Pichincha is highly overall correlated with city_Quito and 1 other fieldsHigh correlation
state_Santa Elena is highly overall correlated with city_SalinasHigh correlation
state_Santo Domingo de los Tsachilas is highly overall correlated with city_Santo DomingoHigh correlation
type_B is highly overall correlated with clusterHigh correlation
type_C is highly overall correlated with clusterHigh correlation
type_D is highly overall correlated with clusterHigh correlation
type_E is highly overall correlated with clusterHigh correlation
year is highly overall correlated with days_after_earthquake and 2 other fieldsHigh correlation
city_Babahoyo is highly imbalanced (87.1%) Imbalance
city_Cayambe is highly imbalanced (87.0%) Imbalance
city_Cuenca is highly imbalanced (69.2%) Imbalance
city_Daule is highly imbalanced (86.3%) Imbalance
city_El Carmen is highly imbalanced (86.7%) Imbalance
city_Esmeraldas is highly imbalanced (86.2%) Imbalance
city_Guaranda is highly imbalanced (86.2%) Imbalance
city_Ibarra is highly imbalanced (86.7%) Imbalance
city_Latacunga is highly imbalanced (76.9%) Imbalance
city_Libertad is highly imbalanced (86.6%) Imbalance
city_Loja is highly imbalanced (86.0%) Imbalance
city_Machala is highly imbalanced (77.7%) Imbalance
city_Manta is highly imbalanced (78.2%) Imbalance
city_Playas is highly imbalanced (86.5%) Imbalance
city_Puyo is highly imbalanced (85.7%) Imbalance
city_Quevedo is highly imbalanced (86.5%) Imbalance
city_Riobamba is highly imbalanced (86.6%) Imbalance
city_Salinas is highly imbalanced (86.8%) Imbalance
city_Santo Domingo is highly imbalanced (68.8%) Imbalance
state_Bolivar is highly imbalanced (86.2%) Imbalance
state_Chimborazo is highly imbalanced (86.6%) Imbalance
state_Cotopaxi is highly imbalanced (76.9%) Imbalance
state_El Oro is highly imbalanced (77.7%) Imbalance
state_Esmeraldas is highly imbalanced (86.2%) Imbalance
state_Imbabura is highly imbalanced (86.7%) Imbalance
state_Loja is highly imbalanced (86.0%) Imbalance
state_Los Rios is highly imbalanced (77.3%) Imbalance
state_Manabi is highly imbalanced (69.9%) Imbalance
state_Pastaza is highly imbalanced (85.7%) Imbalance
state_Santa Elena is highly imbalanced (86.8%) Imbalance
state_Santo Domingo de los Tsachilas is highly imbalanced (68.8%) Imbalance
state_Tungurahua is highly imbalanced (76.4%) Imbalance
type_E is highly imbalanced (61.7%) Imbalance
family_BABY CARE is highly imbalanced (80.1%) Imbalance
family_BEAUTY is highly imbalanced (80.6%) Imbalance
family_BEVERAGES is highly imbalanced (80.4%) Imbalance
family_BOOKS is highly imbalanced (80.9%) Imbalance
family_BREAD/BAKERY is highly imbalanced (80.2%) Imbalance
family_CELEBRATION is highly imbalanced (80.1%) Imbalance
family_CLEANING is highly imbalanced (81.1%) Imbalance
family_DAIRY is highly imbalanced (80.9%) Imbalance
family_DELI is highly imbalanced (80.8%) Imbalance
family_EGGS is highly imbalanced (79.9%) Imbalance
family_FROZEN FOODS is highly imbalanced (80.6%) Imbalance
family_GROCERY I is highly imbalanced (81.0%) Imbalance
family_GROCERY II is highly imbalanced (80.4%) Imbalance
family_HARDWARE is highly imbalanced (81.0%) Imbalance
family_HOME AND KITCHEN I is highly imbalanced (81.3%) Imbalance
family_HOME AND KITCHEN II is highly imbalanced (80.0%) Imbalance
family_HOME APPLIANCES is highly imbalanced (80.5%) Imbalance
family_HOME CARE is highly imbalanced (80.3%) Imbalance
family_LADIESWEAR is highly imbalanced (81.3%) Imbalance
family_LAWN AND GARDEN is highly imbalanced (81.1%) Imbalance
family_LINGERIE is highly imbalanced (80.0%) Imbalance
family_LIQUOR,WINE,BEER is highly imbalanced (80.5%) Imbalance
family_MAGAZINES is highly imbalanced (79.8%) Imbalance
family_MEATS is highly imbalanced (79.4%) Imbalance
family_PERSONAL CARE is highly imbalanced (80.3%) Imbalance
family_PET SUPPLIES is highly imbalanced (80.2%) Imbalance
family_PLAYERS AND ELECTRONICS is highly imbalanced (79.4%) Imbalance
family_POULTRY is highly imbalanced (80.5%) Imbalance
family_PREPARED FOODS is highly imbalanced (80.3%) Imbalance
family_PRODUCE is highly imbalanced (80.1%) Imbalance
family_SCHOOL AND OFFICE SUPPLIES is highly imbalanced (80.1%) Imbalance
family_SEAFOOD is highly imbalanced (80.7%) Imbalance
is_holiday is highly imbalanced (81.9%) Imbalance
is_event is highly imbalanced (78.8%) Imbalance
is_additional is highly imbalanced (86.7%) Imbalance
is_transfer is highly imbalanced (96.3%) Imbalance
is_bridge is highly imbalanced (98.0%) Imbalance
id has unique values Unique
sales has 9305 (31.0%) zeros Zeros
onpromotion has 23792 (79.3%) zeros Zeros
day_of_week has 4273 (14.2%) zeros Zeros
days_to_next_payday has 1984 (6.6%) zeros Zeros
days_after_earthquake has 21160 (70.5%) zeros Zeros

Reproduction

Analysis started2025-03-25 12:01:13.535270
Analysis finished2025-03-25 12:01:41.274454
Duration27.74 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Unique 

Distinct30009
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1509762.7
Minimum104
Maximum3000633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.6 KiB
2025-03-25T14:01:41.348392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile146241
Q1763684
median1513888
Q32266555
95-th percentile2853809
Maximum3000633
Range3000529
Interquartile range (IQR)1502871

Descriptive statistics

Standard deviation867108.32
Coefficient of variation (CV)0.57433419
Kurtosis-1.2002423
Mean1509762.7
Median Absolute Deviation (MAD)751483
Skewness-0.013339911
Sum4.5306468 × 1010
Variance7.5187683 × 1011
MonotonicityNot monotonic
2025-03-25T14:01:41.447842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
928237 1
 
< 0.1%
298189 1
 
< 0.1%
122546 1
 
< 0.1%
1647768 1
 
< 0.1%
844276 1
 
< 0.1%
2178629 1
 
< 0.1%
947306 1
 
< 0.1%
2273742 1
 
< 0.1%
2566179 1
 
< 0.1%
334528 1
 
< 0.1%
Other values (29999) 29999
> 99.9%
ValueCountFrequency (%)
104 1
< 0.1%
369 1
< 0.1%
419 1
< 0.1%
485 1
< 0.1%
637 1
< 0.1%
650 1
< 0.1%
869 1
< 0.1%
984 1
< 0.1%
1211 1
< 0.1%
1245 1
< 0.1%
ValueCountFrequency (%)
3000633 1
< 0.1%
3000525 1
< 0.1%
3000494 1
< 0.1%
3000452 1
< 0.1%
3000408 1
< 0.1%
3000354 1
< 0.1%
3000350 1
< 0.1%
3000290 1
< 0.1%
3000196 1
< 0.1%
3000025 1
< 0.1%

sales
Real number (ℝ)

High correlation  Zeros 

Distinct7090
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean355.80287
Minimum0
Maximum29666
Zeros9305
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size234.6 KiB
2025-03-25T14:01:41.543853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11
Q3197
95-th percentile1945.3196
Maximum29666
Range29666
Interquartile range (IQR)197

Descriptive statistics

Standard deviation1104.4272
Coefficient of variation (CV)3.1040424
Kurtosis75.200019
Mean355.80287
Median Absolute Deviation (MAD)11
Skewness6.9244935
Sum10677288
Variance1219759.4
MonotonicityNot monotonic
2025-03-25T14:01:41.638911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9305
31.0%
1 1146
 
3.8%
2 828
 
2.8%
3 715
 
2.4%
4 622
 
2.1%
5 533
 
1.8%
6 446
 
1.5%
7 382
 
1.3%
8 334
 
1.1%
10 294
 
1.0%
Other values (7080) 15404
51.3%
ValueCountFrequency (%)
0 9305
31.0%
1 1146
 
3.8%
1.548 1
 
< 0.1%
1.962 1
 
< 0.1%
2 828
 
2.8%
2.512 1
 
< 0.1%
2.666 1
 
< 0.1%
2.964 1
 
< 0.1%
3 715
 
2.4%
3.384 1
 
< 0.1%
ValueCountFrequency (%)
29666 1
< 0.1%
21858 1
< 0.1%
20106 1
< 0.1%
20011 1
< 0.1%
18826 1
< 0.1%
17677 1
< 0.1%
17512 1
< 0.1%
17142 1
< 0.1%
15628 1
< 0.1%
15578 1
< 0.1%

onpromotion
Real number (ℝ)

High correlation  Zeros 

Distinct166
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6294778
Minimum0
Maximum626
Zeros23792
Zeros (%)79.3%
Negative0
Negative (%)0.0%
Memory size234.6 KiB
2025-03-25T14:01:41.732625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12
Maximum626
Range626
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.060363
Coefficient of variation (CV)4.9669037
Kurtosis362.53913
Mean2.6294778
Median Absolute Deviation (MAD)0
Skewness13.876918
Sum78908
Variance170.57309
MonotonicityNot monotonic
2025-03-25T14:01:41.827096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23792
79.3%
1 1807
 
6.0%
2 841
 
2.8%
3 455
 
1.5%
4 330
 
1.1%
5 263
 
0.9%
6 242
 
0.8%
7 179
 
0.6%
8 153
 
0.5%
9 136
 
0.5%
Other values (156) 1811
 
6.0%
ValueCountFrequency (%)
0 23792
79.3%
1 1807
 
6.0%
2 841
 
2.8%
3 455
 
1.5%
4 330
 
1.1%
5 263
 
0.9%
6 242
 
0.8%
7 179
 
0.6%
8 153
 
0.5%
9 136
 
0.5%
ValueCountFrequency (%)
626 1
< 0.1%
474 1
< 0.1%
446 1
< 0.1%
275 1
< 0.1%
239 1
< 0.1%
232 1
< 0.1%
231 1
< 0.1%
226 1
< 0.1%
225 1
< 0.1%
222 1
< 0.1%

year
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
2016
6528 
2014
6515 
2015
6452 
2013
6353 
2017
4161 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters120036
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2015
4th row2014
5th row2016

Common Values

ValueCountFrequency (%)
2016 6528
21.8%
2014 6515
21.7%
2015 6452
21.5%
2013 6353
21.2%
2017 4161
13.9%

Length

2025-03-25T14:01:41.910630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:41.985947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2016 6528
21.8%
2014 6515
21.7%
2015 6452
21.5%
2013 6353
21.2%
2017 4161
13.9%

Most occurring characters

ValueCountFrequency (%)
2 30009
25.0%
0 30009
25.0%
1 30009
25.0%
6 6528
 
5.4%
4 6515
 
5.4%
5 6452
 
5.4%
3 6353
 
5.3%
7 4161
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120036
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 30009
25.0%
0 30009
25.0%
1 30009
25.0%
6 6528
 
5.4%
4 6515
 
5.4%
5 6452
 
5.4%
3 6353
 
5.3%
7 4161
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 120036
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 30009
25.0%
0 30009
25.0%
1 30009
25.0%
6 6528
 
5.4%
4 6515
 
5.4%
5 6452
 
5.4%
3 6353
 
5.3%
7 4161
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 30009
25.0%
0 30009
25.0%
1 30009
25.0%
6 6528
 
5.4%
4 6515
 
5.4%
5 6452
 
5.4%
3 6353
 
5.3%
7 4161
 
3.5%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1829118
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.6 KiB
2025-03-25T14:01:42.051963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.383969
Coefficient of variation (CV)0.54730993
Kurtosis-1.1405482
Mean6.1829118
Median Absolute Deviation (MAD)3
Skewness0.11581655
Sum185543
Variance11.451246
MonotonicityNot monotonic
2025-03-25T14:01:42.113555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 2812
9.4%
7 2762
9.2%
4 2731
9.1%
6 2724
9.1%
3 2722
9.1%
5 2675
8.9%
2 2558
8.5%
8 2427
8.1%
9 2206
7.4%
10 2197
7.3%
Other values (2) 4195
14.0%
ValueCountFrequency (%)
1 2812
9.4%
2 2558
8.5%
3 2722
9.1%
4 2731
9.1%
5 2675
8.9%
6 2724
9.1%
7 2762
9.2%
8 2427
8.1%
9 2206
7.4%
10 2197
7.3%
ValueCountFrequency (%)
12 2087
7.0%
11 2108
7.0%
10 2197
7.3%
9 2206
7.4%
8 2427
8.1%
7 2762
9.2%
6 2724
9.1%
5 2675
8.9%
4 2731
9.1%
3 2722
9.1%

day_of_month
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.617015
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.6 KiB
2025-03-25T14:01:42.183586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7781602
Coefficient of variation (CV)0.56208951
Kurtosis-1.1790682
Mean15.617015
Median Absolute Deviation (MAD)8
Skewness0.020192141
Sum468651
Variance77.056097
MonotonicityNot monotonic
2025-03-25T14:01:42.262625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17 1033
 
3.4%
18 1032
 
3.4%
1 1027
 
3.4%
15 1014
 
3.4%
16 1013
 
3.4%
14 1012
 
3.4%
8 1011
 
3.4%
4 1009
 
3.4%
5 1006
 
3.4%
19 1002
 
3.3%
Other values (21) 19850
66.1%
ValueCountFrequency (%)
1 1027
3.4%
2 998
3.3%
3 986
3.3%
4 1009
3.4%
5 1006
3.4%
6 972
3.2%
7 976
3.3%
8 1011
3.4%
9 958
3.2%
10 1000
3.3%
ValueCountFrequency (%)
31 565
1.9%
30 892
3.0%
29 915
3.0%
28 945
3.1%
27 966
3.2%
26 963
3.2%
25 911
3.0%
24 986
3.3%
23 956
3.2%
22 940
3.1%

day_of_week
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0068313
Minimum0
Maximum6
Zeros4273
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size234.6 KiB
2025-03-25T14:01:42.520288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0030103
Coefficient of variation (CV)0.66615319
Kurtosis-1.2575825
Mean3.0068313
Median Absolute Deviation (MAD)2
Skewness-0.0052764876
Sum90232
Variance4.0120501
MonotonicityNot monotonic
2025-03-25T14:01:42.575641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 4365
14.5%
1 4304
14.3%
6 4299
14.3%
4 4288
14.3%
2 4283
14.3%
0 4273
14.2%
3 4197
14.0%
ValueCountFrequency (%)
0 4273
14.2%
1 4304
14.3%
2 4283
14.3%
3 4197
14.0%
4 4288
14.3%
5 4365
14.5%
6 4299
14.3%
ValueCountFrequency (%)
6 4299
14.3%
5 4365
14.5%
4 4288
14.3%
3 4197
14.0%
2 4283
14.3%
1 4304
14.3%
0 4273
14.2%

days_to_next_payday
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1619181
Minimum0
Maximum15
Zeros1984
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size234.6 KiB
2025-03-25T14:01:42.636060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q311
95-th percentile14
Maximum15
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.4279092
Coefficient of variation (CV)0.61825744
Kurtosis-1.2053872
Mean7.1619181
Median Absolute Deviation (MAD)4
Skewness0.00049633311
Sum214922
Variance19.60638
MonotonicityNot monotonic
2025-03-25T14:01:42.700255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
12 2026
 
6.8%
11 2005
 
6.7%
7 2002
 
6.7%
1 2000
 
6.7%
14 1993
 
6.6%
4 1987
 
6.6%
0 1984
 
6.6%
5 1980
 
6.6%
10 1973
 
6.6%
2 1964
 
6.5%
Other values (6) 10095
33.6%
ValueCountFrequency (%)
0 1984
6.6%
1 2000
6.7%
2 1964
6.5%
3 1888
6.3%
4 1987
6.6%
5 1980
6.6%
6 1845
6.1%
7 2002
6.7%
8 1919
6.4%
9 1955
6.5%
ValueCountFrequency (%)
15 554
 
1.8%
14 1993
6.6%
13 1934
6.4%
12 2026
6.8%
11 2005
6.7%
10 1973
6.6%
9 1955
6.5%
8 1919
6.4%
7 2002
6.7%
6 1845
6.1%

days_after_earthquake
Real number (ℝ)

High correlation  Zeros 

Distinct487
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.743277
Minimum0
Maximum487
Zeros21160
Zeros (%)70.5%
Negative0
Negative (%)0.0%
Memory size234.6 KiB
2025-03-25T14:01:42.785359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q374
95-th percentile405
Maximum487
Range487
Interquartile range (IQR)74

Descriptive statistics

Standard deviation134.7687
Coefficient of variation (CV)1.8784855
Kurtosis1.5897067
Mean71.743277
Median Absolute Deviation (MAD)0
Skewness1.7322232
Sum2152944
Variance18162.604
MonotonicityNot monotonic
2025-03-25T14:01:42.881078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21160
70.5%
159 33
 
0.1%
375 32
 
0.1%
80 30
 
0.1%
327 30
 
0.1%
275 29
 
0.1%
105 29
 
0.1%
279 29
 
0.1%
141 28
 
0.1%
201 28
 
0.1%
Other values (477) 8581
28.6%
ValueCountFrequency (%)
0 21160
70.5%
1 20
 
0.1%
2 23
 
0.1%
3 16
 
0.1%
4 14
 
< 0.1%
5 18
 
0.1%
6 23
 
0.1%
7 17
 
0.1%
8 15
 
< 0.1%
9 19
 
0.1%
ValueCountFrequency (%)
487 15
< 0.1%
486 20
0.1%
485 13
< 0.1%
484 20
0.1%
483 25
0.1%
482 8
 
< 0.1%
481 12
< 0.1%
480 18
0.1%
479 20
0.1%
478 21
0.1%

dcoilwtico
Real number (ℝ)

High correlation 

Distinct994
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.750165
Minimum26.19
Maximum110.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.6 KiB
2025-03-25T14:01:42.971791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.19
5-th percentile36.91
Q146.32
median53.33
Q395.71
95-th percentile105.23
Maximum110.62
Range84.43
Interquartile range (IQR)49.39

Descriptive statistics

Standard deviation25.589191
Coefficient of variation (CV)0.37769931
Kurtosis-1.6064475
Mean67.750165
Median Absolute Deviation (MAD)12.64
Skewness0.32088989
Sum2033114.7
Variance654.80669
MonotonicityNot monotonic
2025-03-25T14:01:43.062280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.02 217
 
0.7%
93.12 145
 
0.5%
97.86 138
 
0.5%
104.76 120
 
0.4%
103.83 117
 
0.4%
59.41 110
 
0.4%
48.49 110
 
0.4%
53.19 109
 
0.4%
46.72 104
 
0.3%
47.79 104
 
0.3%
Other values (984) 28735
95.8%
ValueCountFrequency (%)
26.19 13
 
< 0.1%
26.68 12
 
< 0.1%
27.54 21
 
0.1%
27.96 14
 
< 0.1%
28.47 19
 
0.1%
29.05 25
 
0.1%
29.32 77
0.3%
29.45 75
0.2%
29.54 13
 
< 0.1%
29.55 15
 
< 0.1%
ValueCountFrequency (%)
110.62 54
0.2%
110.17 16
 
0.1%
109.62 10
 
< 0.1%
109.11 15
 
< 0.1%
108.72 19
 
0.1%
108.67 25
0.1%
108.51 9
 
< 0.1%
108.5 16
 
0.1%
108.31 61
0.2%
108.23 12
 
< 0.1%

cluster
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.468726
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.6 KiB
2025-03-25T14:01:43.131905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q313
95-th percentile15
Maximum17
Range16
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.6526183
Coefficient of variation (CV)0.54938822
Kurtosis-1.2591823
Mean8.468726
Median Absolute Deviation (MAD)4
Skewness0.046139009
Sum254138
Variance21.646857
MonotonicityNot monotonic
2025-03-25T14:01:43.196516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 3812
12.7%
10 3357
11.2%
6 3323
11.1%
15 2822
9.4%
14 2242
7.5%
13 2164
 
7.2%
4 1766
 
5.9%
1 1686
 
5.6%
8 1643
 
5.5%
11 1611
 
5.4%
Other values (7) 5583
18.6%
ValueCountFrequency (%)
1 1686
5.6%
2 1115
 
3.7%
3 3812
12.7%
4 1766
5.9%
5 564
 
1.9%
6 3323
11.1%
7 1167
 
3.9%
8 1643
5.5%
9 1091
 
3.6%
10 3357
11.2%
ValueCountFrequency (%)
17 523
 
1.7%
16 581
 
1.9%
15 2822
9.4%
14 2242
7.5%
13 2164
7.2%
12 542
 
1.8%
11 1611
5.4%
10 3357
11.2%
9 1091
 
3.6%
8 1643
5.5%

city_Babahoyo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29475 
1
 
534

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29475
98.2%
1 534
 
1.8%

Length

2025-03-25T14:01:43.270919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:43.314867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29475
98.2%
1 534
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 29475
98.2%
1 534
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29475
98.2%
1 534
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29475
98.2%
1 534
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29475
98.2%
1 534
 
1.8%

city_Cayambe
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29468 
1
 
541

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29468
98.2%
1 541
 
1.8%

Length

2025-03-25T14:01:43.368477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:43.413458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29468
98.2%
1 541
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 29468
98.2%
1 541
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29468
98.2%
1 541
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29468
98.2%
1 541
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29468
98.2%
1 541
 
1.8%

city_Cuenca
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
28350 
1
 
1659

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28350
94.5%
1 1659
 
5.5%

Length

2025-03-25T14:01:43.466753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:43.511845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 28350
94.5%
1 1659
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 28350
94.5%
1 1659
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28350
94.5%
1 1659
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28350
94.5%
1 1659
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28350
94.5%
1 1659
 
5.5%

city_Daule
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29430 
1
 
579

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29430
98.1%
1 579
 
1.9%

Length

2025-03-25T14:01:43.566948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:43.610435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29430
98.1%
1 579
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 29430
98.1%
1 579
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29430
98.1%
1 579
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29430
98.1%
1 579
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29430
98.1%
1 579
 
1.9%

city_El Carmen
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29451 
1
 
558

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29451
98.1%
1 558
 
1.9%

Length

2025-03-25T14:01:43.664217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:43.709125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29451
98.1%
1 558
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 29451
98.1%
1 558
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29451
98.1%
1 558
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29451
98.1%
1 558
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29451
98.1%
1 558
 
1.9%

city_Esmeraldas
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29426 
1
 
583

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29426
98.1%
1 583
 
1.9%

Length

2025-03-25T14:01:43.762447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:43.805859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29426
98.1%
1 583
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 29426
98.1%
1 583
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29426
98.1%
1 583
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29426
98.1%
1 583
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29426
98.1%
1 583
 
1.9%

city_Guaranda
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29429 
1
 
580

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29429
98.1%
1 580
 
1.9%

Length

2025-03-25T14:01:43.860535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:43.903961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29429
98.1%
1 580
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 29429
98.1%
1 580
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29429
98.1%
1 580
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29429
98.1%
1 580
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29429
98.1%
1 580
 
1.9%

city_Guayaquil
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
25592 
1
4417 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25592
85.3%
1 4417
 
14.7%

Length

2025-03-25T14:01:43.956810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:44.002425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 25592
85.3%
1 4417
 
14.7%

Most occurring characters

ValueCountFrequency (%)
0 25592
85.3%
1 4417
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25592
85.3%
1 4417
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25592
85.3%
1 4417
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25592
85.3%
1 4417
 
14.7%

city_Ibarra
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29454 
1
 
555

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29454
98.2%
1 555
 
1.8%

Length

2025-03-25T14:01:44.058465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:44.104625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29454
98.2%
1 555
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 29454
98.2%
1 555
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29454
98.2%
1 555
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29454
98.2%
1 555
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29454
98.2%
1 555
 
1.8%

city_Latacunga
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
28884 
1
 
1125

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28884
96.3%
1 1125
 
3.7%

Length

2025-03-25T14:01:44.159546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:44.203383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 28884
96.3%
1 1125
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 28884
96.3%
1 1125
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28884
96.3%
1 1125
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28884
96.3%
1 1125
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28884
96.3%
1 1125
 
3.7%

city_Libertad
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29448 
1
 
561

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29448
98.1%
1 561
 
1.9%

Length

2025-03-25T14:01:44.258159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:44.301745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29448
98.1%
1 561
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 29448
98.1%
1 561
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29448
98.1%
1 561
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29448
98.1%
1 561
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29448
98.1%
1 561
 
1.9%

city_Loja
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29419 
1
 
590

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 29419
98.0%
1 590
 
2.0%

Length

2025-03-25T14:01:44.354852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:44.399729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29419
98.0%
1 590
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 29419
98.0%
1 590
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29419
98.0%
1 590
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29419
98.0%
1 590
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29419
98.0%
1 590
 
2.0%

city_Machala
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
28933 
1
 
1076

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28933
96.4%
1 1076
 
3.6%

Length

2025-03-25T14:01:44.453002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:44.496597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 28933
96.4%
1 1076
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 28933
96.4%
1 1076
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28933
96.4%
1 1076
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28933
96.4%
1 1076
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28933
96.4%
1 1076
 
3.6%

city_Manta
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
28964 
1
 
1045

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28964
96.5%
1 1045
 
3.5%

Length

2025-03-25T14:01:44.551417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:44.595016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 28964
96.5%
1 1045
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 28964
96.5%
1 1045
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28964
96.5%
1 1045
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28964
96.5%
1 1045
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28964
96.5%
1 1045
 
3.5%

city_Playas
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29444 
1
 
565

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29444
98.1%
1 565
 
1.9%

Length

2025-03-25T14:01:44.648340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:44.696163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29444
98.1%
1 565
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 29444
98.1%
1 565
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29444
98.1%
1 565
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29444
98.1%
1 565
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29444
98.1%
1 565
 
1.9%

city_Puyo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29402 
1
 
607

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29402
98.0%
1 607
 
2.0%

Length

2025-03-25T14:01:44.750909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:44.794449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29402
98.0%
1 607
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 29402
98.0%
1 607
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29402
98.0%
1 607
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29402
98.0%
1 607
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29402
98.0%
1 607
 
2.0%

city_Quevedo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29441 
1
 
568

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29441
98.1%
1 568
 
1.9%

Length

2025-03-25T14:01:44.849034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:44.895454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29441
98.1%
1 568
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 29441
98.1%
1 568
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29441
98.1%
1 568
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29441
98.1%
1 568
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29441
98.1%
1 568
 
1.9%

city_Quito
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
20098 
1
9911 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 20098
67.0%
1 9911
33.0%

Length

2025-03-25T14:01:44.951520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:44.999929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 20098
67.0%
1 9911
33.0%

Most occurring characters

ValueCountFrequency (%)
0 20098
67.0%
1 9911
33.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20098
67.0%
1 9911
33.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20098
67.0%
1 9911
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20098
67.0%
1 9911
33.0%

city_Riobamba
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29449 
1
 
560

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29449
98.1%
1 560
 
1.9%

Length

2025-03-25T14:01:45.056728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:45.100144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29449
98.1%
1 560
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 29449
98.1%
1 560
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29449
98.1%
1 560
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29449
98.1%
1 560
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29449
98.1%
1 560
 
1.9%

city_Salinas
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29457 
1
 
552

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29457
98.2%
1 552
 
1.8%

Length

2025-03-25T14:01:45.155904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:45.199383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29457
98.2%
1 552
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 29457
98.2%
1 552
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29457
98.2%
1 552
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29457
98.2%
1 552
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29457
98.2%
1 552
 
1.8%

city_Santo Domingo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
28323 
1
 
1686

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28323
94.4%
1 1686
 
5.6%

Length

2025-03-25T14:01:45.252218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:45.297053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 28323
94.4%
1 1686
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 28323
94.4%
1 1686
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28323
94.4%
1 1686
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28323
94.4%
1 1686
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28323
94.4%
1 1686
 
5.6%

state_Bolivar
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29429 
1
 
580

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29429
98.1%
1 580
 
1.9%

Length

2025-03-25T14:01:45.351249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:45.394662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29429
98.1%
1 580
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 29429
98.1%
1 580
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29429
98.1%
1 580
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29429
98.1%
1 580
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29429
98.1%
1 580
 
1.9%

state_Chimborazo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29449 
1
 
560

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29449
98.1%
1 560
 
1.9%

Length

2025-03-25T14:01:45.449055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:45.492430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29449
98.1%
1 560
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 29449
98.1%
1 560
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29449
98.1%
1 560
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29449
98.1%
1 560
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29449
98.1%
1 560
 
1.9%

state_Cotopaxi
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
28884 
1
 
1125

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28884
96.3%
1 1125
 
3.7%

Length

2025-03-25T14:01:45.545383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:45.590387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 28884
96.3%
1 1125
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 28884
96.3%
1 1125
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28884
96.3%
1 1125
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28884
96.3%
1 1125
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28884
96.3%
1 1125
 
3.7%

state_El Oro
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
28933 
1
 
1076

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28933
96.4%
1 1076
 
3.6%

Length

2025-03-25T14:01:45.643547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:45.687054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 28933
96.4%
1 1076
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 28933
96.4%
1 1076
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28933
96.4%
1 1076
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28933
96.4%
1 1076
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28933
96.4%
1 1076
 
3.6%

state_Esmeraldas
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29426 
1
 
583

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29426
98.1%
1 583
 
1.9%

Length

2025-03-25T14:01:45.741814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:45.785115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29426
98.1%
1 583
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 29426
98.1%
1 583
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29426
98.1%
1 583
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29426
98.1%
1 583
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29426
98.1%
1 583
 
1.9%

state_Guayas
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
23887 
1
6122 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23887
79.6%
1 6122
 
20.4%

Length

2025-03-25T14:01:45.838367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:45.884772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23887
79.6%
1 6122
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 23887
79.6%
1 6122
 
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23887
79.6%
1 6122
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23887
79.6%
1 6122
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23887
79.6%
1 6122
 
20.4%

state_Imbabura
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29454 
1
 
555

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29454
98.2%
1 555
 
1.8%

Length

2025-03-25T14:01:45.941184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:45.984799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29454
98.2%
1 555
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 29454
98.2%
1 555
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29454
98.2%
1 555
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29454
98.2%
1 555
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29454
98.2%
1 555
 
1.8%

state_Loja
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29419 
1
 
590

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 29419
98.0%
1 590
 
2.0%

Length

2025-03-25T14:01:46.039427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:46.083160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29419
98.0%
1 590
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 29419
98.0%
1 590
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29419
98.0%
1 590
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29419
98.0%
1 590
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29419
98.0%
1 590
 
2.0%

state_Los Rios
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
28907 
1
 
1102

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28907
96.3%
1 1102
 
3.7%

Length

2025-03-25T14:01:46.135911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:46.181272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 28907
96.3%
1 1102
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 28907
96.3%
1 1102
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28907
96.3%
1 1102
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28907
96.3%
1 1102
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28907
96.3%
1 1102
 
3.7%

state_Manabi
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
28406 
1
 
1603

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28406
94.7%
1 1603
 
5.3%

Length

2025-03-25T14:01:46.235849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:46.279331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 28406
94.7%
1 1603
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 28406
94.7%
1 1603
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28406
94.7%
1 1603
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28406
94.7%
1 1603
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28406
94.7%
1 1603
 
5.3%

state_Pastaza
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29402 
1
 
607

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29402
98.0%
1 607
 
2.0%

Length

2025-03-25T14:01:46.333712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:46.377605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29402
98.0%
1 607
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 29402
98.0%
1 607
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29402
98.0%
1 607
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29402
98.0%
1 607
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29402
98.0%
1 607
 
2.0%

state_Pichincha
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
19557 
1
10452 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 19557
65.2%
1 10452
34.8%

Length

2025-03-25T14:01:46.430197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:46.476200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19557
65.2%
1 10452
34.8%

Most occurring characters

ValueCountFrequency (%)
0 19557
65.2%
1 10452
34.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19557
65.2%
1 10452
34.8%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19557
65.2%
1 10452
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19557
65.2%
1 10452
34.8%

state_Santa Elena
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29457 
1
 
552

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29457
98.2%
1 552
 
1.8%

Length

2025-03-25T14:01:46.532594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:46.576653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29457
98.2%
1 552
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 29457
98.2%
1 552
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29457
98.2%
1 552
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29457
98.2%
1 552
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29457
98.2%
1 552
 
1.8%

state_Santo Domingo de los Tsachilas
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
28323 
1
 
1686

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28323
94.4%
1 1686
 
5.6%

Length

2025-03-25T14:01:46.630954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:46.674758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 28323
94.4%
1 1686
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 28323
94.4%
1 1686
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28323
94.4%
1 1686
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28323
94.4%
1 1686
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28323
94.4%
1 1686
 
5.6%

state_Tungurahua
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
28852 
1
 
1157

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28852
96.1%
1 1157
 
3.9%

Length

2025-03-25T14:01:46.727412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:46.772533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 28852
96.1%
1 1157
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 28852
96.1%
1 1157
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28852
96.1%
1 1157
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28852
96.1%
1 1157
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28852
96.1%
1 1157
 
3.9%

type_B
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
25571 
1
4438 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25571
85.2%
1 4438
 
14.8%

Length

2025-03-25T14:01:46.825492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:46.869906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 25571
85.2%
1 4438
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 25571
85.2%
1 4438
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25571
85.2%
1 4438
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25571
85.2%
1 4438
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25571
85.2%
1 4438
 
14.8%

type_C
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
21666 
1
8343 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21666
72.2%
1 8343
 
27.8%

Length

2025-03-25T14:01:46.927043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:46.971803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 21666
72.2%
1 8343
 
27.8%

Most occurring characters

ValueCountFrequency (%)
0 21666
72.2%
1 8343
 
27.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21666
72.2%
1 8343
 
27.8%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21666
72.2%
1 8343
 
27.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21666
72.2%
1 8343
 
27.8%

type_D
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
19963 
1
10046 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 19963
66.5%
1 10046
33.5%

Length

2025-03-25T14:01:47.026769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:47.073788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19963
66.5%
1 10046
33.5%

Most occurring characters

ValueCountFrequency (%)
0 19963
66.5%
1 10046
33.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19963
66.5%
1 10046
33.5%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19963
66.5%
1 10046
33.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19963
66.5%
1 10046
33.5%

type_E
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
27767 
1
 
2242

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 27767
92.5%
1 2242
 
7.5%

Length

2025-03-25T14:01:47.129309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:47.173882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 27767
92.5%
1 2242
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 27767
92.5%
1 2242
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27767
92.5%
1 2242
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27767
92.5%
1 2242
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27767
92.5%
1 2242
 
7.5%

family_BABY CARE
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29083 
1
 
926

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29083
96.9%
1 926
 
3.1%

Length

2025-03-25T14:01:47.228339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:47.273080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29083
96.9%
1 926
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 29083
96.9%
1 926
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29083
96.9%
1 926
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29083
96.9%
1 926
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29083
96.9%
1 926
 
3.1%

family_BEAUTY
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29112 
1
 
897

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 29112
97.0%
1 897
 
3.0%

Length

2025-03-25T14:01:47.327146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:47.371472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29112
97.0%
1 897
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 29112
97.0%
1 897
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29112
97.0%
1 897
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29112
97.0%
1 897
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29112
97.0%
1 897
 
3.0%

family_BEVERAGES
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29097 
1
 
912

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29097
97.0%
1 912
 
3.0%

Length

2025-03-25T14:01:47.424323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:47.468165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29097
97.0%
1 912
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 29097
97.0%
1 912
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29097
97.0%
1 912
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29097
97.0%
1 912
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29097
97.0%
1 912
 
3.0%

family_BOOKS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29130 
1
 
879

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 29130
97.1%
1 879
 
2.9%

Length

2025-03-25T14:01:47.522583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:47.566464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29130
97.1%
1 879
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 29130
97.1%
1 879
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29130
97.1%
1 879
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29130
97.1%
1 879
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29130
97.1%
1 879
 
2.9%

family_BREAD/BAKERY
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29086 
1
 
923

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29086
96.9%
1 923
 
3.1%

Length

2025-03-25T14:01:47.620460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:47.664443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29086
96.9%
1 923
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 29086
96.9%
1 923
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29086
96.9%
1 923
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29086
96.9%
1 923
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29086
96.9%
1 923
 
3.1%

family_CELEBRATION
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29080 
1
 
929

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29080
96.9%
1 929
 
3.1%

Length

2025-03-25T14:01:47.717932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:47.763215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29080
96.9%
1 929
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 29080
96.9%
1 929
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29080
96.9%
1 929
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29080
96.9%
1 929
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29080
96.9%
1 929
 
3.1%

family_CLEANING
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29143 
1
 
866

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29143
97.1%
1 866
 
2.9%

Length

2025-03-25T14:01:47.818957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:47.862737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29143
97.1%
1 866
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 29143
97.1%
1 866
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29143
97.1%
1 866
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29143
97.1%
1 866
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29143
97.1%
1 866
 
2.9%

family_DAIRY
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29131 
1
 
878

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29131
97.1%
1 878
 
2.9%

Length

2025-03-25T14:01:48.083365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:48.126630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29131
97.1%
1 878
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 29131
97.1%
1 878
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29131
97.1%
1 878
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29131
97.1%
1 878
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29131
97.1%
1 878
 
2.9%

family_DELI
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29124 
1
 
885

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29124
97.1%
1 885
 
2.9%

Length

2025-03-25T14:01:48.179466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:48.224509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29124
97.1%
1 885
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 29124
97.1%
1 885
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29124
97.1%
1 885
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29124
97.1%
1 885
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29124
97.1%
1 885
 
2.9%

family_EGGS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29069 
1
 
940

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29069
96.9%
1 940
 
3.1%

Length

2025-03-25T14:01:48.277581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:48.320999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29069
96.9%
1 940
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 29069
96.9%
1 940
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29069
96.9%
1 940
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29069
96.9%
1 940
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29069
96.9%
1 940
 
3.1%

family_FROZEN FOODS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29111 
1
 
898

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29111
97.0%
1 898
 
3.0%

Length

2025-03-25T14:01:48.375677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:48.419491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29111
97.0%
1 898
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 29111
97.0%
1 898
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29111
97.0%
1 898
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29111
97.0%
1 898
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29111
97.0%
1 898
 
3.0%

family_GROCERY I
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29134 
1
 
875

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29134
97.1%
1 875
 
2.9%

Length

2025-03-25T14:01:48.472407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:48.517406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29134
97.1%
1 875
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 29134
97.1%
1 875
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29134
97.1%
1 875
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29134
97.1%
1 875
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29134
97.1%
1 875
 
2.9%

family_GROCERY II
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29097 
1
 
912

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29097
97.0%
1 912
 
3.0%

Length

2025-03-25T14:01:48.571060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:48.614691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29097
97.0%
1 912
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 29097
97.0%
1 912
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29097
97.0%
1 912
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29097
97.0%
1 912
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29097
97.0%
1 912
 
3.0%

family_HARDWARE
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29135 
1
 
874

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29135
97.1%
1 874
 
2.9%

Length

2025-03-25T14:01:48.669173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:48.712678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29135
97.1%
1 874
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 29135
97.1%
1 874
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29135
97.1%
1 874
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29135
97.1%
1 874
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29135
97.1%
1 874
 
2.9%

family_HOME AND KITCHEN I
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29151 
1
 
858

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29151
97.1%
1 858
 
2.9%

Length

2025-03-25T14:01:48.766885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:48.810707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29151
97.1%
1 858
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 29151
97.1%
1 858
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29151
97.1%
1 858
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29151
97.1%
1 858
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29151
97.1%
1 858
 
2.9%

family_HOME AND KITCHEN II
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29072 
1
 
937

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29072
96.9%
1 937
 
3.1%

Length

2025-03-25T14:01:48.863583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:48.908479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29072
96.9%
1 937
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 29072
96.9%
1 937
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29072
96.9%
1 937
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29072
96.9%
1 937
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29072
96.9%
1 937
 
3.1%

family_HOME APPLIANCES
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29108 
1
 
901

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29108
97.0%
1 901
 
3.0%

Length

2025-03-25T14:01:48.961686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:49.005207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29108
97.0%
1 901
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 29108
97.0%
1 901
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29108
97.0%
1 901
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29108
97.0%
1 901
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29108
97.0%
1 901
 
3.0%

family_HOME CARE
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29091 
1
 
918

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29091
96.9%
1 918
 
3.1%

Length

2025-03-25T14:01:49.060153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:49.103801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29091
96.9%
1 918
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 29091
96.9%
1 918
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29091
96.9%
1 918
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29091
96.9%
1 918
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29091
96.9%
1 918
 
3.1%

family_LADIESWEAR
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29153 
1
 
856

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29153
97.1%
1 856
 
2.9%

Length

2025-03-25T14:01:49.156972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:49.202115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29153
97.1%
1 856
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 29153
97.1%
1 856
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29153
97.1%
1 856
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29153
97.1%
1 856
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29153
97.1%
1 856
 
2.9%

family_LAWN AND GARDEN
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29140 
1
 
869

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29140
97.1%
1 869
 
2.9%

Length

2025-03-25T14:01:49.255538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:49.299323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29140
97.1%
1 869
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 29140
97.1%
1 869
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29140
97.1%
1 869
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29140
97.1%
1 869
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29140
97.1%
1 869
 
2.9%

family_LINGERIE
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29078 
1
 
931

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29078
96.9%
1 931
 
3.1%

Length

2025-03-25T14:01:49.354397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:49.398180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29078
96.9%
1 931
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 29078
96.9%
1 931
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29078
96.9%
1 931
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29078
96.9%
1 931
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29078
96.9%
1 931
 
3.1%

family_LIQUOR,WINE,BEER
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29107 
1
 
902

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29107
97.0%
1 902
 
3.0%

Length

2025-03-25T14:01:49.451266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:49.496439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29107
97.0%
1 902
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 29107
97.0%
1 902
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29107
97.0%
1 902
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29107
97.0%
1 902
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29107
97.0%
1 902
 
3.0%

family_MAGAZINES
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29064 
1
 
945

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29064
96.9%
1 945
 
3.1%

Length

2025-03-25T14:01:49.549759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:49.595731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29064
96.9%
1 945
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 29064
96.9%
1 945
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29064
96.9%
1 945
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29064
96.9%
1 945
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29064
96.9%
1 945
 
3.1%

family_MEATS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29041 
1
 
968

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29041
96.8%
1 968
 
3.2%

Length

2025-03-25T14:01:49.652204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:49.696032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29041
96.8%
1 968
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 29041
96.8%
1 968
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29041
96.8%
1 968
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29041
96.8%
1 968
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29041
96.8%
1 968
 
3.2%

family_PERSONAL CARE
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29091 
1
 
918

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29091
96.9%
1 918
 
3.1%

Length

2025-03-25T14:01:49.751313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:49.795213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29091
96.9%
1 918
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 29091
96.9%
1 918
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29091
96.9%
1 918
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29091
96.9%
1 918
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29091
96.9%
1 918
 
3.1%

family_PET SUPPLIES
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29085 
1
 
924

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29085
96.9%
1 924
 
3.1%

Length

2025-03-25T14:01:49.848375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:49.892029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29085
96.9%
1 924
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 29085
96.9%
1 924
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29085
96.9%
1 924
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29085
96.9%
1 924
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29085
96.9%
1 924
 
3.1%

family_PLAYERS AND ELECTRONICS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29038 
1
 
971

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29038
96.8%
1 971
 
3.2%

Length

2025-03-25T14:01:49.947217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:49.991108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29038
96.8%
1 971
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 29038
96.8%
1 971
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29038
96.8%
1 971
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29038
96.8%
1 971
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29038
96.8%
1 971
 
3.2%

family_POULTRY
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29108 
1
 
901

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29108
97.0%
1 901
 
3.0%

Length

2025-03-25T14:01:50.045742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:50.089607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29108
97.0%
1 901
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 29108
97.0%
1 901
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29108
97.0%
1 901
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29108
97.0%
1 901
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29108
97.0%
1 901
 
3.0%

family_PREPARED FOODS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29094 
1
 
915

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29094
97.0%
1 915
 
3.0%

Length

2025-03-25T14:01:50.142535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:50.187875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29094
97.0%
1 915
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 29094
97.0%
1 915
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29094
97.0%
1 915
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29094
97.0%
1 915
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29094
97.0%
1 915
 
3.0%

family_PRODUCE
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29081 
1
 
928

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29081
96.9%
1 928
 
3.1%

Length

2025-03-25T14:01:50.241051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:50.284986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29081
96.9%
1 928
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 29081
96.9%
1 928
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29081
96.9%
1 928
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29081
96.9%
1 928
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29081
96.9%
1 928
 
3.1%

family_SCHOOL AND OFFICE SUPPLIES
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29079 
1
 
930

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29079
96.9%
1 930
 
3.1%

Length

2025-03-25T14:01:50.340030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:50.384120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29079
96.9%
1 930
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 29079
96.9%
1 930
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29079
96.9%
1 930
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29079
96.9%
1 930
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29079
96.9%
1 930
 
3.1%

family_SEAFOOD
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0
29120 
1
 
889

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30009
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29120
97.0%
1 889
 
3.0%

Length

2025-03-25T14:01:50.436970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:50.482545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 29120
97.0%
1 889
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 29120
97.0%
1 889
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30009
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29120
97.0%
1 889
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29120
97.0%
1 889
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29120
97.0%
1 889
 
3.0%

is_holiday
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0.0
29190 
1.0
 
819

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90027
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 29190
97.3%
1.0 819
 
2.7%

Length

2025-03-25T14:01:50.535690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:50.579992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 29190
97.3%
1.0 819
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 59199
65.8%
. 30009
33.3%
1 819
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60018
66.7%
Other Punctuation 30009
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59199
98.6%
1 819
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 30009
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90027
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59199
65.8%
. 30009
33.3%
1 819
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59199
65.8%
. 30009
33.3%
1 819
 
0.9%

is_event
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0.0
29003 
1.0
 
1006

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90027
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 29003
96.6%
1.0 1006
 
3.4%

Length

2025-03-25T14:01:50.635349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:50.679580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 29003
96.6%
1.0 1006
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 59012
65.5%
. 30009
33.3%
1 1006
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60018
66.7%
Other Punctuation 30009
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59012
98.3%
1 1006
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 30009
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90027
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59012
65.5%
. 30009
33.3%
1 1006
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59012
65.5%
. 30009
33.3%
1 1006
 
1.1%

is_additional
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0.0
29452 
1.0
 
557

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90027
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 29452
98.1%
1.0 557
 
1.9%

Length

2025-03-25T14:01:50.733084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:50.778852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 29452
98.1%
1.0 557
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 59461
66.0%
. 30009
33.3%
1 557
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60018
66.7%
Other Punctuation 30009
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59461
99.1%
1 557
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 30009
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90027
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59461
66.0%
. 30009
33.3%
1 557
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59461
66.0%
. 30009
33.3%
1 557
 
0.6%

is_transfer
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0.0
29890 
1.0
 
119

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90027
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 29890
99.6%
1.0 119
 
0.4%

Length

2025-03-25T14:01:50.833081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:50.877330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 29890
99.6%
1.0 119
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 59899
66.5%
. 30009
33.3%
1 119
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60018
66.7%
Other Punctuation 30009
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59899
99.8%
1 119
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 30009
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90027
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59899
66.5%
. 30009
33.3%
1 119
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59899
66.5%
. 30009
33.3%
1 119
 
0.1%

is_bridge
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.6 KiB
0.0
29951 
1.0
 
58

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90027
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 29951
99.8%
1.0 58
 
0.2%

Length

2025-03-25T14:01:50.932649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T14:01:50.976773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 29951
99.8%
1.0 58
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 59960
66.6%
. 30009
33.3%
1 58
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60018
66.7%
Other Punctuation 30009
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59960
99.9%
1 58
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 30009
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90027
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59960
66.6%
. 30009
33.3%
1 58
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59960
66.6%
. 30009
33.3%
1 58
 
0.1%

Interactions

2025-03-25T14:01:39.330558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:32.394158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:33.289700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:34.048997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:34.803531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.519053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:36.269584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.005684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.768526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:38.648480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:39.407962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:32.485872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:33.367911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:34.127039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:34.876463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.594693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:36.347277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.084071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.844393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:38.717132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:39.483979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:32.563514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:33.442692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:34.203677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:34.949677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.670579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:36.420504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.160844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.918589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:38.787671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:39.562048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:32.754863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:33.519994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:34.279778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.021486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.747749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:36.495819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.239950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.993369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:38.855982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:39.634978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:32.829230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:33.593662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:34.353203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.090332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.821128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:36.567272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.312461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:38.213437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:38.922134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:39.712792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:32.907234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:33.670635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:34.428830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.162882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.894040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:36.641849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.390549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:38.286666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:38.991469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:39.787389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:32.983112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:33.746017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:34.503343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.234789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.968694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:36.713065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.465621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:38.359110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:39.060945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:39.865932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:33.064120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:33.825217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:34.583037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.307636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:36.045413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:36.790232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.544244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:38.435604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:39.131058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:39.943969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:33.139714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:33.900054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:34.656991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.379813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:36.118521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:36.862204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.619278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:38.505811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:39.200033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:40.018893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:33.211575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:33.971452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:34.726806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:35.445855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:36.188421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:36.931151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:37.691274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:38.573802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T14:01:39.261415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-25T14:01:51.136259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
city_Babahoyocity_Cayambecity_Cuencacity_Daulecity_El Carmencity_Esmeraldascity_Guarandacity_Guayaquilcity_Ibarracity_Latacungacity_Libertadcity_Lojacity_Machalacity_Mantacity_Playascity_Puyocity_Quevedocity_Quitocity_Riobambacity_Salinascity_Santo Domingoclusterday_of_monthday_of_weekdays_after_earthquakedays_to_next_paydaydcoilwticofamily_BABY CAREfamily_BEAUTYfamily_BEVERAGESfamily_BOOKSfamily_BREAD/BAKERYfamily_CELEBRATIONfamily_CLEANINGfamily_DAIRYfamily_DELIfamily_EGGSfamily_FROZEN FOODSfamily_GROCERY Ifamily_GROCERY IIfamily_HARDWAREfamily_HOME AND KITCHEN Ifamily_HOME AND KITCHEN IIfamily_HOME APPLIANCESfamily_HOME CAREfamily_LADIESWEARfamily_LAWN AND GARDENfamily_LINGERIEfamily_LIQUOR,WINE,BEERfamily_MAGAZINESfamily_MEATSfamily_PERSONAL CAREfamily_PET SUPPLIESfamily_PLAYERS AND ELECTRONICSfamily_POULTRYfamily_PREPARED FOODSfamily_PRODUCEfamily_SCHOOL AND OFFICE SUPPLIESfamily_SEAFOODidis_additionalis_bridgeis_eventis_holidayis_transfermonthonpromotionsalesstate_Bolivarstate_Chimborazostate_Cotopaxistate_El Orostate_Esmeraldasstate_Guayasstate_Imbaburastate_Lojastate_Los Riosstate_Manabistate_Pastazastate_Pichinchastate_Santa Elenastate_Santo Domingo de los Tsachilasstate_Tungurahuatype_Btype_Ctype_Dtype_Eyear
city_Babahoyo1.0000.0160.0310.0170.0170.0170.0170.0550.0170.0250.0170.0170.0250.0240.0170.0180.0170.0940.0170.0170.0320.3790.0070.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0060.0000.0030.0000.0000.0040.0060.0000.0010.0000.0000.0030.0000.0000.0000.0030.0000.0010.0000.0000.0040.0060.0000.0100.0000.0000.0000.0000.0000.0170.0170.0250.0250.0170.0680.0170.0170.6890.0310.0180.0980.0170.0320.0260.3230.0830.0950.0370.011
city_Cayambe0.0161.0000.0320.0170.0170.0170.0170.0560.0170.0250.0170.0170.0250.0240.0170.0180.0170.0950.0170.0170.0320.3230.0100.0000.0000.0070.0000.0000.0000.0130.0000.0000.0000.0040.0000.0030.0050.0000.0000.0000.0030.0110.0090.0040.0000.0000.0090.0000.0000.0000.0000.0000.0100.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0200.0170.0170.0250.0250.0170.0680.0170.0170.0250.0310.0180.1850.0170.0320.0260.3250.0840.0960.0380.000
city_Cuenca0.0310.0321.0000.0330.0320.0330.0330.1000.0320.0470.0320.0330.0460.0450.0320.0340.0330.1700.0320.0320.0580.5160.0090.0100.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0100.0000.0070.0000.0000.0060.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0100.0100.0100.0090.0170.0330.0320.0470.0460.0330.1220.0320.0330.0460.0570.0340.1770.0320.0580.0480.1220.1500.1730.0680.000
city_Daule0.0170.0170.0331.0000.0170.0180.0180.0580.0170.0260.0180.0180.0260.0250.0180.0180.0180.0980.0180.0170.0330.4370.0140.0100.0080.0000.0130.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0100.0000.0000.0000.0000.0040.0000.0040.0000.0000.0000.0120.0000.0000.0000.0100.0000.0000.0000.0080.0180.0180.0260.0260.0180.2770.0170.0180.0260.0320.0180.1020.0170.0330.0270.0580.0870.1970.0390.016
city_El Carmen0.0170.0170.0320.0171.0000.0180.0180.0570.0170.0260.0170.0180.0250.0250.0170.0180.0170.0960.0170.0170.0330.2880.0000.0000.0020.0000.0210.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0080.0090.0000.0000.0000.0000.0000.0000.0000.0050.0050.0000.0000.0000.0000.0080.0000.0000.0050.0080.0000.0000.0000.0000.0000.0000.0000.0000.0110.0580.0000.0180.0170.0260.0250.0180.0690.0170.0180.0260.5790.0180.1000.0170.0330.0260.0570.2210.0970.0380.000
city_Esmeraldas0.0170.0170.0330.0180.0181.0000.0180.0580.0170.0270.0180.0180.0260.0250.0180.0180.0180.0980.0180.0170.0330.3960.0100.0000.0040.0000.0000.0000.0000.0000.0000.0070.0000.0060.0000.0030.0000.0000.0030.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0060.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0180.0180.0270.0260.9990.0710.0170.0180.0260.0320.0180.1020.0170.0330.0270.0580.0870.0990.4950.006
city_Guaranda0.0170.0170.0330.0180.0180.0181.0000.0580.0170.0260.0180.0180.0260.0250.0180.0180.0180.0980.0180.0170.0330.3110.0000.0050.0000.0070.0080.0030.0000.0060.0000.0000.0000.0000.0000.0000.0000.0040.0030.0000.0030.0000.0050.0000.0000.0000.0000.0070.0000.0000.0000.0000.0030.0000.0000.0000.0070.0000.0000.0000.0000.0000.0080.0100.0000.0110.0000.0000.9990.0180.0260.0260.0180.0710.0170.0180.0260.0320.0180.1020.0170.0330.0270.0580.2260.0990.0390.000
city_Guayaquil0.0550.0560.1000.0580.0570.0580.0581.0000.0560.0820.0570.0580.0800.0780.0570.0590.0570.2920.0570.0560.1010.4700.0060.0000.0080.0190.0000.0000.0060.0000.0090.0080.0000.0000.0000.0070.0000.0000.0070.0060.0000.0000.0000.0060.0000.0000.0070.0000.0000.0000.0000.0050.0130.0000.0000.0000.0000.0020.0060.0040.0090.0000.0000.0090.0070.0000.0110.0160.0580.0570.0820.0800.0580.8210.0560.0580.0810.0980.0590.3040.0560.1010.0830.0220.0280.0680.2750.006
city_Ibarra0.0170.0170.0320.0170.0170.0170.0170.0561.0000.0260.0170.0180.0250.0250.0170.0180.0170.0960.0170.0170.0320.3040.0100.0090.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0060.0030.0000.0000.0000.0100.0000.0030.0030.0000.0020.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0090.0000.0170.0170.0260.0250.0170.0690.9990.0180.0250.0320.0180.1000.0170.0320.0260.0570.2210.0970.0380.001
city_Latacunga0.0250.0250.0470.0260.0260.0270.0260.0820.0261.0000.0260.0270.0370.0370.0260.0270.0260.1380.0260.0260.0470.4380.0180.0000.0000.0000.0180.0060.0000.0090.0000.0130.0000.0030.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0040.0070.0000.0000.0090.0000.0000.0020.0030.0060.0000.0100.0100.0000.0000.0000.0180.0260.0261.0000.0370.0270.1000.0260.0270.0380.0460.0270.1440.0260.0470.0390.0820.3180.1400.0550.004
city_Libertad0.0170.0170.0320.0180.0170.0180.0180.0570.0170.0261.0000.0180.0250.0250.0170.0180.0170.0960.0170.0170.0330.3890.0000.0000.0000.0000.0230.0000.0000.0000.0010.0090.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0130.0120.0000.0000.0000.0000.0000.0000.0000.0180.0170.0260.0250.0180.2720.0170.0180.0260.0320.0180.1000.0170.0330.0260.0570.0850.0970.4850.000
city_Loja0.0170.0170.0330.0180.0180.0180.0180.0580.0180.0270.0181.0000.0260.0260.0180.0190.0180.0990.0180.0180.0340.2960.0120.0000.0000.0000.0100.0000.0000.0000.0000.0000.0090.0030.0000.0000.0000.0000.0000.0030.0000.0000.0000.0050.0000.0070.0030.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0030.0000.0000.0050.0000.0000.0110.0000.0000.0180.0180.0270.0260.0180.0710.0180.9990.0260.0330.0190.1030.0180.0340.0270.0580.0870.1990.0390.000
city_Machala0.0250.0250.0460.0260.0250.0260.0260.0800.0250.0370.0250.0261.0000.0360.0250.0260.0250.1350.0250.0250.0460.4030.0140.0100.0060.0000.0000.0000.0000.0000.0130.0030.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0020.0000.0000.0070.0090.0000.0000.0010.0000.0090.0000.0000.0000.0010.0000.0000.0010.0110.0000.0000.0000.0000.0000.0260.0250.0371.0000.0260.0970.0250.0260.0370.0450.0260.1410.0250.0460.0380.0800.0830.0790.0540.007
city_Manta0.0240.0240.0450.0250.0250.0250.0250.0780.0250.0370.0250.0260.0361.0000.0250.0260.0250.1330.0250.0250.0460.4640.0000.0000.0000.0000.0000.0080.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0100.0000.0090.0000.0000.0090.0000.0000.0000.0000.0080.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0410.0180.0250.0250.0370.0360.0250.0960.0250.0260.0360.7990.0260.1390.0250.0460.0370.0790.1180.0790.0530.004
city_Playas0.0170.0170.0320.0180.0170.0180.0180.0570.0170.0260.0170.0180.0250.0251.0000.0180.0170.0970.0170.0170.0330.2890.0020.0000.0000.0070.0100.0000.0000.0000.0070.0000.0000.0090.0000.0000.0000.0030.0000.0000.0000.0090.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0020.0100.0000.0120.0000.0160.0180.0170.0260.0250.0180.2730.0170.0180.0260.0320.0180.1010.0170.0330.0260.0570.2230.0980.0380.000
city_Puyo0.0180.0180.0340.0180.0180.0180.0180.0590.0180.0270.0180.0190.0260.0260.0181.0000.0180.1000.0180.0180.0340.3420.0100.0050.0080.0070.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0040.0000.0110.0000.0170.0000.0000.0060.0000.0090.0000.0000.0000.0010.0000.0120.0000.0180.0180.0180.0270.0260.0180.0720.0180.0190.0270.0330.9990.1050.0180.0340.0280.0590.2310.1020.0400.000
city_Quevedo0.0170.0170.0330.0180.0170.0180.0180.0570.0170.0260.0170.0180.0250.0250.0170.0181.0000.0970.0170.0170.0330.2900.0000.0090.0070.0000.0000.0060.0050.0000.0000.0000.0000.0050.0000.0000.0020.0000.0000.0000.0050.0000.0000.0000.0000.0000.0040.0000.0000.0020.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0090.0000.0000.0020.0000.0000.0000.0000.0180.0170.0260.0250.0180.0700.0170.0180.7110.0320.0180.1010.0170.0330.0270.0570.2230.0980.0390.000
city_Quito0.0940.0950.1700.0980.0960.0980.0980.2920.0960.1380.0960.0990.1350.1330.0970.1000.0971.0000.0960.0960.1710.6750.0080.0100.0180.0100.0020.0000.0000.0000.0000.0000.0000.0070.0120.0000.0080.0000.0000.0000.0000.0020.0000.0000.0000.0050.0000.0000.0080.0090.0000.0000.0060.0000.0050.0000.0000.0010.0000.0080.0000.0060.0000.0000.0000.0000.0280.1050.0980.0960.1380.1350.0980.3550.0960.0990.1370.1670.1000.9610.0960.1710.1400.0450.2610.0660.1990.016
city_Riobamba0.0170.0170.0320.0180.0170.0180.0180.0570.0170.0260.0170.0180.0250.0250.0170.0180.0170.0961.0000.0170.0330.3280.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0060.0000.0030.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0040.0030.0030.0000.0000.0000.0180.9990.0260.0250.0180.0690.0170.0180.0260.0320.0180.1000.0170.0330.0260.0570.2220.0970.0380.000
city_Salinas0.0170.0170.0320.0170.0170.0170.0170.0560.0170.0260.0170.0180.0250.0250.0170.0180.0170.0960.0171.0000.0320.4260.0090.0080.0090.0000.0000.0000.0010.0000.0070.0000.0000.0000.0000.0050.0000.0080.0030.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0020.0170.0170.0260.0250.0170.0690.0170.0180.0250.0310.0180.1000.9990.0320.0260.0560.0840.1930.0380.000
city_Santo Domingo0.0320.0320.0580.0330.0330.0330.0330.1010.0320.0470.0330.0340.0460.0460.0330.0340.0330.1710.0330.0321.0000.3570.0000.0140.0000.0070.0040.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0060.0000.0000.0000.0000.0060.0060.0040.0000.0000.0050.0000.0000.0060.0000.0000.0000.0080.0000.0000.0000.0000.0260.0330.0330.0470.0460.0330.1230.0320.0340.0470.0570.0340.1780.0321.0000.0480.1250.0160.0120.0690.000
cluster0.3790.3230.5160.4370.2880.3960.3110.4700.3040.4380.3890.2960.4030.4640.2890.3420.2900.6750.3280.4260.3571.000-0.014-0.0060.0060.0110.0050.0130.0000.0180.0000.0000.0000.0060.0120.0000.0150.0000.0140.0040.0080.0130.0110.0000.0000.0000.0130.0000.0000.0060.0000.0040.0000.0050.0190.0000.0000.0000.0000.0030.0000.0130.0040.0130.0000.0040.0020.0340.3110.3280.4380.4030.3960.5580.3040.2960.3100.3840.3420.6630.4260.3570.3600.7610.6170.8640.8000.008
day_of_month0.0070.0100.0090.0140.0000.0100.0000.0060.0100.0180.0000.0120.0140.0000.0020.0100.0000.0080.0080.0090.000-0.0141.0000.0020.006-0.468-0.0050.0000.0140.0000.0100.0200.0180.0060.0100.0000.0000.0080.0120.0130.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0160.0250.0110.1340.0800.0500.1790.0920.0020.0090.0040.0000.0080.0180.0140.0100.0110.0100.0120.0100.0000.0100.0110.0090.0000.0000.0130.0140.0000.0090.013
day_of_week0.0000.0000.0100.0100.0000.0000.0050.0000.0090.0000.0000.0000.0100.0000.0000.0050.0090.0100.0000.0080.014-0.0060.0021.0000.0040.002-0.0040.0000.0000.0040.0130.0000.0000.0000.0090.0010.0110.0000.0140.0000.0000.0000.0050.0000.0050.0000.0090.0060.0000.0000.0050.0000.0050.0070.0100.0070.0000.0000.0130.0020.0720.1070.0470.0620.125-0.0080.0070.0140.0050.0000.0000.0100.0000.0030.0090.0000.0000.0000.0050.0110.0080.0140.0020.0000.0100.0000.0000.002
days_after_earthquake0.0120.0000.0000.0080.0020.0040.0000.0080.0000.0000.0000.0000.0060.0000.0000.0080.0070.0180.0000.0090.0000.0060.0060.0041.000-0.006-0.3950.0000.0100.0000.0000.0000.0120.0000.0000.0080.0170.0030.0000.0210.0070.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.8060.1360.0930.0970.0730.1100.0140.3640.1550.0000.0000.0000.0060.0040.0060.0000.0000.0000.0070.0080.0150.0090.0000.0090.0140.0120.0000.0000.599
days_to_next_payday0.0000.0070.0000.0000.0000.0000.0070.0190.0080.0000.0000.0000.0000.0000.0070.0070.0000.0100.0000.0000.0070.011-0.4680.002-0.0061.0000.0060.0140.0080.0230.0000.0090.0090.0000.0000.0000.0000.0000.0150.0150.0000.0090.0080.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0020.0220.018-0.0110.1210.0630.0670.1220.1030.008-0.0010.0070.0070.0000.0000.0000.0000.0140.0080.0000.0070.0000.0070.0080.0000.0070.0060.0210.0000.0000.0000.000
dcoilwtico0.0000.0000.0000.0130.0210.0000.0080.0000.0000.0180.0230.0100.0000.0000.0100.0180.0000.0020.0000.0000.0040.005-0.005-0.004-0.3950.0061.0000.0110.0000.0000.0000.0000.0000.0000.0160.0090.0080.0000.0050.0020.0000.0100.0060.0120.0060.0000.0000.0000.0000.0120.0000.0100.0010.0000.0080.0000.0000.0080.000-0.7430.1400.0670.1440.0510.047-0.004-0.278-0.1850.0080.0000.0180.0000.0000.0000.0000.0100.0000.0000.0180.0080.0000.0040.0000.0050.0120.0130.0120.561
family_BABY CARE0.0000.0000.0000.0000.0000.0000.0030.0000.0000.0060.0000.0000.0000.0080.0000.0000.0060.0000.0000.0000.0000.0130.0000.0000.0000.0140.0111.0000.0300.0300.0300.0310.0310.0300.0300.0300.0310.0300.0300.0300.0300.0290.0310.0300.0310.0290.0300.0310.0300.0310.0320.0310.0310.0320.0300.0310.0310.0310.0300.0040.0000.0000.0100.0020.0000.0000.0070.0270.0030.0000.0060.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0030.0120.0000.000
family_BEAUTY0.0000.0000.0160.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0080.0000.0000.0050.0000.0000.0010.0030.0000.0140.0000.0100.0080.0000.0301.0000.0300.0290.0300.0300.0290.0290.0290.0300.0300.0290.0300.0290.0290.0300.0300.0300.0290.0290.0300.0300.0310.0310.0300.0300.0310.0300.0300.0300.0300.0300.0000.0000.0000.0000.0060.0000.0000.0060.0260.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0010.0030.0000.0000.0050.0000.0090.000
family_BEVERAGES0.0000.0130.0000.0000.0000.0000.0060.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0040.0000.0230.0000.0300.0301.0000.0300.0300.0310.0290.0300.0300.0310.0300.0300.0300.0300.0290.0310.0300.0300.0290.0290.0310.0300.0310.0310.0300.0300.0310.0300.0300.0310.0310.0300.0070.0000.0000.0000.0050.0010.0000.0310.2460.0060.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0100.0060.0000.000
family_BOOKS0.0000.0000.0000.0140.0000.0000.0000.0090.0000.0000.0010.0000.0130.0000.0070.0000.0000.0000.0000.0070.0000.0000.0100.0130.0000.0000.0000.0300.0290.0301.0000.0300.0300.0290.0290.0290.0300.0290.0290.0300.0290.0290.0300.0290.0300.0290.0290.0300.0290.0300.0310.0300.0300.0310.0290.0300.0300.0300.0290.0120.0000.0000.0000.0000.0000.0040.0060.0260.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0050.0000.005
family_BREAD/BAKERY0.0000.0000.0000.0000.0080.0070.0000.0080.0000.0130.0090.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0090.0000.0310.0300.0300.0301.0000.0310.0300.0300.0300.0310.0300.0300.0300.0300.0290.0310.0300.0310.0290.0300.0310.0300.0310.0310.0310.0310.0320.0300.0300.0310.0310.0300.0000.0000.0000.0120.0000.0000.0000.0150.0270.0000.0000.0130.0030.0070.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.000
family_CELEBRATION0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0120.0090.0000.0310.0300.0310.0300.0311.0000.0300.0300.0300.0310.0300.0300.0310.0300.0300.0310.0300.0310.0290.0300.0310.0300.0310.0320.0310.0310.0320.0300.0310.0310.0310.0300.0130.0000.0000.0000.0000.0030.0100.0070.0270.0000.0000.0000.0000.0000.0000.0000.0090.0050.0000.0000.0000.0000.0000.0000.0000.0000.0030.0030.013
family_CLEANING0.0000.0040.0000.0000.0000.0060.0000.0000.0000.0030.0000.0030.0040.0000.0090.0000.0050.0070.0000.0000.0000.0060.0060.0000.0000.0000.0000.0300.0290.0290.0290.0300.0301.0000.0290.0290.0300.0290.0290.0290.0290.0280.0300.0290.0290.0280.0290.0300.0290.0300.0300.0290.0300.0300.0290.0290.0300.0300.0290.0000.0000.0000.0000.0000.0000.0000.0030.0040.0000.0000.0030.0040.0060.0010.0000.0030.0040.0000.0000.0030.0000.0000.0050.0070.0000.0000.0000.000
family_DAIRY0.0000.0000.0100.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0120.0100.0090.0000.0000.0160.0300.0290.0300.0290.0300.0300.0291.0000.0290.0300.0290.0290.0300.0290.0290.0300.0290.0300.0290.0290.0300.0290.0300.0310.0300.0300.0310.0290.0300.0300.0300.0290.0050.0000.0000.0000.0050.0020.0180.0460.0040.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0140.0000.0000.0010.0000.0060.0000.0000.005
family_DELI0.0000.0030.0000.0000.0000.0030.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0010.0080.0000.0090.0300.0290.0300.0290.0300.0300.0290.0291.0000.0300.0290.0290.0300.0290.0290.0300.0300.0300.0290.0290.0300.0300.0300.0310.0300.0300.0310.0300.0300.0300.0300.0290.0120.0000.0000.0000.0000.0150.0060.0370.0260.0000.0000.0000.0000.0030.0080.0000.0000.0000.0000.0000.0000.0050.0000.0000.0090.0000.0000.0120.011
family_EGGS0.0080.0050.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0020.0080.0000.0000.0000.0150.0000.0110.0170.0000.0080.0310.0300.0310.0300.0310.0310.0300.0300.0301.0000.0300.0300.0310.0300.0300.0310.0310.0310.0300.0300.0310.0310.0310.0320.0310.0310.0320.0310.0310.0310.0310.0300.0000.0000.0000.0050.0000.0000.0000.0070.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0040.0000.0000.0000.0000.0060.0050.0000.013
family_FROZEN FOODS0.0000.0000.0000.0010.0080.0000.0040.0000.0060.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0080.0000.0000.0080.0000.0030.0000.0000.0300.0300.0300.0290.0300.0300.0290.0290.0290.0301.0000.0290.0300.0290.0290.0300.0300.0300.0290.0290.0300.0300.0310.0310.0300.0300.0310.0300.0300.0300.0300.0300.0090.0000.0000.0040.0000.0000.0000.0060.0220.0040.0000.0000.0000.0000.0000.0060.0000.0050.0030.0000.0000.0080.0000.0000.0000.0000.0060.0000.000
family_GROCERY I0.0000.0000.0000.0000.0090.0030.0030.0070.0030.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0030.0000.0140.0120.0140.0000.0150.0050.0300.0290.0300.0290.0300.0300.0290.0290.0290.0300.0291.0000.0300.0290.0290.0300.0290.0300.0290.0290.0300.0290.0300.0310.0300.0300.0310.0290.0300.0300.0300.0290.0000.0080.0000.0040.0000.0020.0050.2180.5300.0030.0000.0000.0000.0030.0070.0030.0000.0000.0130.0000.0000.0030.0000.0000.0030.0000.0000.0030.000
family_GROCERY II0.0060.0000.0060.0000.0000.0000.0000.0060.0000.0000.0000.0030.0000.0000.0000.0090.0000.0000.0000.0000.0000.0040.0130.0000.0210.0150.0020.0300.0300.0300.0300.0300.0310.0290.0300.0300.0310.0300.0301.0000.0300.0290.0310.0300.0300.0290.0290.0310.0300.0310.0310.0300.0300.0310.0300.0300.0310.0310.0300.0130.0000.0000.0000.0000.0030.0000.0060.0270.0000.0000.0000.0000.0000.0050.0000.0030.0000.0000.0090.0000.0000.0000.0000.0050.0000.0050.0000.012
family_HARDWARE0.0000.0030.0040.0000.0000.0000.0030.0000.0000.0060.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0080.0080.0000.0070.0000.0000.0300.0290.0300.0290.0300.0300.0290.0290.0290.0300.0290.0290.0301.0000.0290.0300.0290.0300.0290.0290.0300.0290.0300.0310.0300.0300.0310.0290.0300.0300.0300.0290.0000.0000.0000.0000.0000.0020.0000.0060.0260.0030.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0020.0000.0010.0020.000
family_HOME AND KITCHEN I0.0030.0110.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0100.0090.0000.0000.0020.0060.0000.0000.0130.0000.0000.0160.0090.0100.0290.0290.0290.0290.0290.0300.0280.0290.0290.0300.0290.0290.0290.0291.0000.0300.0290.0290.0280.0280.0300.0290.0300.0300.0290.0290.0300.0290.0290.0300.0300.0290.0160.0000.0000.0000.0000.0030.0000.0050.0260.0000.0060.0000.0000.0050.0020.0000.0000.0000.0070.0000.0080.0000.0000.0090.0130.0000.0100.0000.003
family_HOME AND KITCHEN II0.0000.0090.0000.0000.0000.0000.0050.0000.0100.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0050.0000.0080.0060.0310.0300.0310.0300.0310.0310.0300.0300.0300.0310.0300.0300.0310.0300.0301.0000.0300.0310.0300.0300.0310.0310.0310.0320.0310.0310.0320.0300.0310.0310.0310.0300.0000.0000.0000.0000.0000.0000.0030.0070.0270.0050.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0040.0000.0000.0000.0150.0060.0000.0000.000
family_HOME APPLIANCES0.0000.0040.0000.0000.0000.0000.0000.0060.0000.0000.0000.0050.0000.0090.0000.0000.0000.0000.0060.0090.0070.0000.0000.0000.0000.0070.0120.0300.0300.0300.0290.0300.0300.0290.0290.0300.0310.0300.0290.0300.0290.0290.0301.0000.0300.0290.0290.0300.0300.0310.0310.0300.0300.0310.0300.0300.0300.0300.0300.0000.0000.0000.0060.0000.0000.0000.0060.0270.0000.0060.0000.0000.0000.0020.0000.0050.0000.0060.0000.0000.0090.0070.0100.0100.0050.0000.0000.000
family_HOME CARE0.0040.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0050.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0060.0310.0300.0300.0300.0310.0310.0290.0300.0300.0310.0300.0300.0300.0300.0290.0310.0301.0000.0290.0300.0310.0300.0310.0310.0300.0310.0310.0300.0300.0310.0310.0300.0040.0000.0000.0000.0000.0030.0040.0070.0270.0000.0000.0000.0050.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0010.0070.0000.0060.0040.000
family_LADIESWEAR0.0060.0000.0000.0100.0000.0000.0000.0000.0030.0000.0000.0070.0020.0000.0000.0000.0000.0050.0030.0000.0060.0000.0000.0000.0000.0000.0000.0290.0290.0290.0290.0290.0290.0280.0290.0290.0300.0290.0290.0290.0290.0280.0300.0290.0291.0000.0280.0300.0290.0300.0300.0290.0290.0300.0290.0290.0290.0300.0290.0070.0040.0000.0000.0080.0000.0000.0050.0260.0000.0030.0000.0020.0000.0010.0030.0070.0030.0000.0000.0060.0000.0060.0000.0070.0080.0000.0000.000
family_LAWN AND GARDEN0.0000.0090.0000.0000.0050.0000.0000.0070.0000.0070.0000.0030.0000.0090.0000.0000.0040.0000.0000.0000.0000.0130.0000.0090.0000.0000.0000.0300.0290.0290.0290.0300.0300.0290.0290.0290.0300.0290.0290.0290.0290.0280.0300.0290.0300.0281.0000.0300.0290.0300.0300.0300.0300.0300.0290.0290.0300.0300.0290.0150.0000.0000.0080.0000.0000.0000.0060.0260.0000.0000.0070.0000.0000.0070.0000.0030.0070.0000.0000.0050.0000.0000.0000.0150.0160.0000.0000.000
family_LINGERIE0.0010.0000.0000.0000.0050.0000.0070.0000.0020.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0310.0300.0310.0300.0310.0310.0300.0300.0300.0310.0300.0300.0310.0300.0300.0310.0300.0310.0300.0301.0000.0300.0310.0320.0310.0310.0320.0300.0310.0310.0310.0300.0000.0100.0050.0000.0000.0100.0000.0070.0270.0070.0000.0000.0000.0000.0000.0020.0000.0050.0070.0060.0000.0000.0000.0000.0000.0000.0000.0000.000
family_LIQUOR,WINE,BEER0.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0300.0300.0290.0300.0300.0290.0290.0300.0310.0300.0290.0300.0290.0290.0310.0300.0300.0290.0290.0301.0000.0310.0310.0300.0300.0310.0300.0300.0300.0300.0300.0100.0020.0050.0050.0000.0000.0000.0060.0270.0000.0000.0000.0070.0000.0000.0000.0000.0000.0030.0000.0080.0000.0000.0000.0040.0050.0000.0000.010
family_MAGAZINES0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0020.0090.0110.0000.0000.0060.0000.0000.0000.0000.0120.0310.0310.0310.0300.0310.0310.0300.0300.0300.0310.0310.0300.0310.0300.0300.0310.0310.0310.0300.0300.0310.0311.0000.0320.0310.0310.0320.0310.0310.0310.0310.0300.0000.0000.0000.0040.0000.0000.0070.0070.0270.0000.0110.0000.0090.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.000
family_MEATS0.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0060.0000.0000.0050.0000.0000.0000.0320.0310.0310.0310.0310.0320.0300.0310.0310.0320.0310.0310.0310.0310.0300.0320.0310.0310.0300.0300.0320.0310.0321.0000.0310.0310.0320.0310.0310.0320.0320.0310.0100.0000.0000.0040.0090.0130.0000.0080.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0060.0000.0000.0030.0000.0070.000
family_PERSONAL CARE0.0000.0000.0000.0000.0000.0000.0000.0050.0000.0040.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0060.0040.0000.0000.0000.0000.0100.0310.0300.0300.0300.0310.0310.0290.0300.0300.0310.0300.0300.0300.0300.0290.0310.0300.0300.0290.0300.0310.0300.0310.0311.0000.0310.0310.0300.0300.0310.0310.0300.0000.0000.0000.0060.0000.0030.0020.0000.0270.0000.0000.0040.0000.0000.0020.0000.0000.0000.0100.0000.0000.0000.0060.0040.0000.0000.0000.0000.009
family_PET SUPPLIES0.0000.0100.0000.0000.0080.0000.0030.0130.0000.0070.0000.0000.0010.0000.0000.0110.0000.0060.0000.0000.0040.0000.0000.0050.0000.0000.0010.0310.0300.0300.0300.0310.0310.0300.0300.0300.0310.0300.0300.0300.0300.0290.0310.0300.0310.0290.0300.0310.0300.0310.0310.0311.0000.0320.0300.0310.0310.0310.0300.0000.0000.0000.0000.0000.0000.0070.0070.0270.0030.0000.0070.0010.0000.0070.0000.0000.0000.0000.0110.0000.0000.0040.0080.0070.0010.0000.0020.000
family_PLAYERS AND ELECTRONICS0.0000.0000.0000.0040.0000.0090.0000.0000.0050.0000.0080.0000.0000.0060.0000.0000.0030.0000.0000.0000.0000.0050.0000.0070.0000.0000.0000.0320.0310.0310.0310.0320.0320.0300.0310.0310.0320.0310.0310.0310.0310.0300.0320.0310.0310.0300.0300.0320.0310.0320.0320.0310.0321.0000.0310.0310.0320.0320.0310.0030.0000.0000.0000.0000.0080.0070.0080.0280.0000.0000.0000.0000.0090.0000.0050.0000.0040.0090.0000.0030.0000.0000.0000.0000.0000.0100.0130.006
family_POULTRY0.0030.0000.0020.0000.0000.0060.0000.0000.0000.0000.0000.0000.0090.0000.0030.0170.0000.0050.0000.0010.0000.0190.0000.0100.0000.0000.0080.0300.0300.0300.0290.0300.0300.0290.0290.0300.0310.0300.0290.0300.0290.0290.0300.0300.0300.0290.0290.0300.0300.0310.0310.0300.0300.0311.0000.0300.0300.0300.0300.0030.0060.0000.0000.0000.0000.0000.0060.0270.0000.0000.0000.0090.0060.0000.0000.0000.0000.0000.0170.0030.0010.0000.0000.0000.0000.0000.0000.000
family_PREPARED FOODS0.0000.0000.0000.0040.0050.0000.0000.0000.0000.0090.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0140.0070.0110.0110.0000.0310.0300.0300.0300.0300.0310.0290.0300.0300.0310.0300.0300.0300.0300.0290.0310.0300.0300.0290.0290.0310.0300.0310.0310.0300.0310.0310.0301.0000.0310.0310.0300.0000.0000.0110.0020.0000.0100.0000.0070.0270.0000.0000.0090.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0050.0060.0000.0070.0050.0000.000
family_PRODUCE0.0010.0020.0000.0000.0080.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0310.0300.0310.0300.0310.0310.0300.0300.0300.0310.0300.0300.0310.0300.0300.0310.0300.0310.0290.0300.0310.0300.0310.0320.0310.0310.0320.0300.0311.0000.0310.0300.0000.0000.0000.0100.0020.0000.0000.1990.1290.0070.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0120.0000.0000.0000.0000.000
family_SCHOOL AND OFFICE SUPPLIES0.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0060.0000.0010.0000.0000.0000.0000.0160.0000.0000.0220.0080.0310.0300.0310.0300.0310.0310.0300.0300.0300.0310.0300.0300.0310.0300.0300.0310.0300.0310.0300.0300.0310.0300.0310.0320.0310.0310.0320.0300.0310.0311.0000.0300.0000.0000.0000.0000.0000.0000.0000.0070.0270.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0060.0040.0000.0000.0000.0000.0000.0000.0000.000
family_SEAFOOD0.0000.0000.0000.0000.0000.0000.0000.0060.0000.0020.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0060.0000.0250.0130.0000.0180.0000.0300.0300.0300.0290.0300.0300.0290.0290.0290.0300.0300.0290.0300.0290.0290.0300.0300.0300.0290.0290.0300.0300.0300.0310.0300.0300.0310.0300.0300.0300.0301.0000.0000.0000.0000.0000.0000.0000.0030.0060.0260.0000.0000.0020.0010.0000.0060.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0050.0000.0000.000
id0.0040.0000.0000.0120.0000.0180.0000.0040.0000.0030.0130.0030.0000.0000.0000.0090.0000.0080.0000.0000.0000.0030.0110.0020.806-0.011-0.7430.0040.0000.0070.0120.0000.0130.0000.0050.0120.0000.0090.0000.0130.0000.0160.0000.0000.0040.0070.0150.0000.0100.0000.0100.0000.0000.0030.0030.0000.0000.0000.0001.0000.1210.0900.3180.0600.0760.0670.4030.2180.0000.0000.0030.0000.0180.0080.0000.0030.0000.0000.0090.0070.0000.0000.0050.0100.0000.0000.0110.891
is_additional0.0060.0000.0000.0000.0000.0000.0000.0090.0000.0060.0120.0000.0000.0000.0000.0000.0090.0000.0050.0000.0000.0000.1340.0720.1360.1210.1400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0040.0000.0100.0020.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.1211.0000.1030.0000.0200.0000.2700.0000.0000.0000.0050.0060.0000.0000.0120.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.035
is_bridge0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0060.0000.0000.0000.0130.0800.1070.0930.0630.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0050.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0900.1031.0000.0020.0000.0000.0760.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0030.0000.0000.0040.0000.0000.0000.0000.0080.0060.0070.031
is_event0.0100.0000.0000.0000.0000.0000.0080.0000.0000.0100.0000.0050.0110.0100.0020.0000.0000.0000.0040.0000.0080.0040.0500.0470.0970.0670.1440.0100.0000.0000.0000.0120.0000.0000.0000.0000.0050.0040.0040.0000.0000.0000.0000.0060.0000.0000.0080.0000.0050.0040.0040.0060.0000.0000.0000.0020.0100.0000.0000.3180.0000.0021.0000.0080.0090.2510.0530.0350.0080.0040.0100.0110.0000.0000.0000.0050.0070.0030.0000.0000.0000.0080.0000.0050.0040.0070.0000.200
is_holiday0.0000.0070.0100.0100.0000.0000.0100.0090.0030.0100.0000.0000.0000.0000.0100.0010.0020.0000.0030.0000.0000.0130.1790.0620.0730.1220.0510.0020.0060.0050.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0020.0000.0000.0600.0200.0000.0081.0000.0070.1020.0000.0000.0100.0030.0100.0000.0000.0050.0030.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.024
is_transfer0.0000.0000.0100.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0920.1250.1100.1030.0470.0000.0000.0010.0000.0000.0030.0000.0020.0150.0000.0000.0020.0030.0020.0030.0000.0000.0030.0000.0000.0100.0000.0000.0130.0030.0000.0080.0000.0100.0000.0000.0000.0760.0000.0000.0090.0071.0000.0880.0000.0140.0000.0030.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0070.0050.0000.073
month0.0000.0000.0100.0000.0110.0000.0110.0000.0000.0000.0000.0110.0000.0000.0120.0120.0000.0000.0000.0000.0000.0040.002-0.0080.0140.008-0.0040.0000.0000.0000.0040.0000.0100.0000.0180.0060.0000.0000.0050.0000.0000.0000.0030.0000.0040.0000.0000.0000.0000.0070.0000.0020.0070.0070.0000.0000.0000.0000.0030.0670.2700.0760.2510.1020.0881.0000.0470.0380.0110.0000.0000.0000.0000.0000.0000.0110.0000.0000.0120.0000.0000.0000.0070.0080.0000.0070.0080.133
onpromotion0.0000.0000.0090.0000.0580.0000.0000.0110.0090.0000.0000.0000.0000.0410.0000.0000.0000.0280.0000.0100.0000.0020.0090.0070.364-0.001-0.2780.0070.0060.0310.0060.0150.0070.0030.0460.0370.0070.0060.2180.0060.0060.0050.0070.0060.0070.0050.0060.0070.0060.0070.0080.0000.0070.0080.0060.0070.1990.0070.0060.4030.0000.0000.0530.0000.0000.0471.0000.5430.0000.0000.0000.0000.0000.0100.0090.0000.0000.0480.0000.0270.0100.0000.0000.0220.0240.0290.0150.044
sales0.0000.0200.0170.0080.0000.0000.0000.0160.0000.0180.0000.0000.0000.0180.0160.0180.0000.1050.0000.0020.0260.0340.0040.0140.1550.007-0.1850.0270.0260.2460.0260.0270.0270.0040.0040.0260.0270.0220.5300.0270.0260.0260.0270.0270.0270.0260.0260.0270.0270.0270.0260.0270.0270.0280.0270.0270.1290.0270.0260.2180.0000.0000.0350.0000.0140.0380.5431.0000.0000.0000.0180.0000.0000.0270.0000.0000.0000.0230.0180.1090.0020.0260.0000.0000.0670.0300.0090.033
state_Bolivar0.0170.0170.0330.0180.0180.0180.9990.0580.0170.0260.0180.0180.0260.0250.0180.0180.0180.0980.0180.0170.0330.3110.0000.0050.0000.0070.0080.0030.0000.0060.0000.0000.0000.0000.0000.0000.0000.0040.0030.0000.0030.0000.0050.0000.0000.0000.0000.0070.0000.0000.0000.0000.0030.0000.0000.0000.0070.0000.0000.0000.0000.0000.0080.0100.0000.0110.0000.0001.0000.0180.0260.0260.0180.0710.0170.0180.0260.0320.0180.1020.0170.0330.0270.0580.2260.0990.0390.000
state_Chimborazo0.0170.0170.0320.0180.0170.0180.0180.0570.0170.0260.0170.0180.0250.0250.0170.0180.0170.0960.9990.0170.0330.3280.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0060.0000.0030.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0040.0030.0030.0000.0000.0000.0181.0000.0260.0250.0180.0690.0170.0180.0260.0320.0180.1000.0170.0330.0260.0570.2220.0970.0380.000
state_Cotopaxi0.0250.0250.0470.0260.0260.0270.0260.0820.0261.0000.0260.0270.0370.0370.0260.0270.0260.1380.0260.0260.0470.4380.0180.0000.0000.0000.0180.0060.0000.0090.0000.0130.0000.0030.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0040.0070.0000.0000.0090.0000.0000.0020.0030.0060.0000.0100.0100.0000.0000.0000.0180.0260.0261.0000.0370.0270.1000.0260.0270.0380.0460.0270.1440.0260.0470.0390.0820.3180.1400.0550.004
state_El Oro0.0250.0250.0460.0260.0250.0260.0260.0800.0250.0370.0250.0261.0000.0360.0250.0260.0250.1350.0250.0250.0460.4030.0140.0100.0060.0000.0000.0000.0000.0000.0130.0030.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0020.0000.0000.0070.0090.0000.0000.0010.0000.0090.0000.0000.0000.0010.0000.0000.0010.0110.0000.0000.0000.0000.0000.0260.0250.0371.0000.0260.0970.0250.0260.0370.0450.0260.1410.0250.0460.0380.0800.0830.0790.0540.007
state_Esmeraldas0.0170.0170.0330.0180.0180.9990.0180.0580.0170.0270.0180.0180.0260.0250.0180.0180.0180.0980.0180.0170.0330.3960.0100.0000.0040.0000.0000.0000.0000.0000.0000.0070.0000.0060.0000.0030.0000.0000.0030.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0060.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0180.0180.0270.0261.0000.0710.0170.0180.0260.0320.0180.1020.0170.0330.0270.0580.0870.0990.4950.006
state_Guayas0.0680.0680.1220.2770.0690.0710.0710.8210.0690.1000.2720.0710.0970.0960.2730.0720.0700.3550.0690.0690.1230.5580.0110.0030.0060.0140.0000.0000.0040.0000.0000.0040.0000.0010.0000.0080.0000.0000.0070.0050.0000.0020.0000.0020.0000.0010.0070.0000.0000.0000.0000.0020.0070.0000.0000.0000.0000.0070.0060.0080.0120.0000.0000.0050.0020.0000.0100.0270.0710.0690.1000.0970.0711.0000.0690.0710.0980.1200.0720.3700.0690.1230.1010.0780.0060.0580.3780.009
state_Imbabura0.0170.0170.0320.0170.0170.0170.0170.0560.9990.0260.0170.0180.0250.0250.0170.0180.0170.0960.0170.0170.0320.3040.0100.0090.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0060.0030.0000.0000.0000.0100.0000.0030.0030.0000.0020.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0090.0000.0170.0170.0260.0250.0170.0691.0000.0180.0250.0320.0180.1000.0170.0320.0260.0570.2210.0970.0380.001
state_Loja0.0170.0170.0330.0180.0180.0180.0180.0580.0180.0270.0180.9990.0260.0260.0180.0190.0180.0990.0180.0180.0340.2960.0120.0000.0000.0000.0100.0000.0000.0000.0000.0000.0090.0030.0000.0000.0000.0000.0000.0030.0000.0000.0000.0050.0000.0070.0030.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0030.0000.0000.0050.0000.0000.0110.0000.0000.0180.0180.0270.0260.0180.0710.0181.0000.0260.0330.0190.1030.0180.0340.0270.0580.0870.1990.0390.000
state_Los Rios0.6890.0250.0460.0260.0260.0260.0260.0810.0250.0380.0260.0260.0370.0360.0260.0270.7110.1370.0260.0250.0470.3100.0100.0000.0000.0070.0000.0000.0000.0000.0000.0000.0050.0040.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0030.0070.0050.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0130.0030.0070.0000.0000.0000.0000.0000.0260.0260.0380.0370.0260.0980.0250.0261.0000.0460.0270.1420.0250.0470.0380.1850.1030.1380.0550.000
state_Manabi0.0310.0310.0570.0320.5790.0320.0320.0980.0320.0460.0320.0330.0450.7990.0320.0330.0320.1670.0320.0310.0570.3840.0000.0000.0070.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0130.0000.0000.0070.0000.0060.0000.0000.0000.0070.0030.0000.0000.0100.0000.0090.0000.0000.0080.0000.0000.0000.0000.0000.0030.0000.0000.0000.0480.0230.0320.0320.0460.0450.0320.1200.0320.0330.0461.0000.0330.1730.0310.0570.0470.0990.0370.0010.0670.005
state_Pastaza0.0180.0180.0340.0180.0180.0180.0180.0590.0180.0270.0180.0190.0260.0260.0180.9990.0180.1000.0180.0180.0340.3420.0100.0050.0080.0070.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0040.0000.0110.0000.0170.0000.0000.0060.0000.0090.0000.0000.0000.0010.0000.0120.0000.0180.0180.0180.0270.0260.0180.0720.0180.0190.0270.0331.0000.1050.0180.0340.0280.0590.2310.1020.0400.000
state_Pichincha0.0980.1850.1770.1020.1000.1020.1020.3040.1000.1440.1000.1030.1410.1390.1010.1050.1010.9610.1000.1000.1780.6630.0110.0110.0150.0080.0080.0000.0000.0000.0000.0000.0000.0030.0140.0000.0040.0000.0000.0000.0050.0080.0040.0000.0000.0060.0050.0000.0080.0080.0000.0000.0000.0030.0030.0000.0000.0040.0000.0070.0000.0040.0000.0000.0000.0000.0270.1090.1020.1000.1440.1410.1020.3700.1000.1030.1420.1730.1051.0000.1000.1780.1460.1360.2810.0380.2080.014
state_Santa Elena0.0170.0170.0320.0170.0170.0170.0170.0560.0170.0260.0170.0180.0250.0250.0170.0180.0170.0960.0170.9990.0320.4260.0090.0080.0090.0000.0000.0000.0010.0000.0070.0000.0000.0000.0000.0050.0000.0080.0030.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0020.0170.0170.0260.0250.0170.0690.0170.0180.0250.0310.0180.1001.0000.0320.0260.0560.0840.1930.0380.000
state_Santo Domingo de los Tsachilas0.0320.0320.0580.0330.0330.0330.0330.1010.0320.0470.0330.0340.0460.0460.0330.0340.0330.1710.0330.0321.0000.3570.0000.0140.0000.0070.0040.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0060.0000.0000.0000.0000.0060.0060.0040.0000.0000.0050.0000.0000.0060.0000.0000.0000.0080.0000.0000.0000.0000.0260.0330.0330.0470.0460.0330.1230.0320.0340.0470.0570.0340.1780.0321.0000.0480.1250.0160.0120.0690.000
state_Tungurahua0.0260.0260.0480.0270.0260.0270.0270.0830.0260.0390.0260.0270.0380.0370.0260.0280.0270.1400.0260.0260.0480.3600.0000.0020.0090.0060.0000.0000.0000.0000.0000.0000.0000.0050.0010.0000.0000.0000.0000.0000.0000.0090.0000.0100.0010.0000.0000.0000.0000.0000.0000.0040.0080.0000.0000.0060.0120.0000.0000.0050.0000.0000.0000.0000.0000.0070.0000.0000.0270.0260.0390.0380.0270.1010.0260.0270.0380.0470.0280.1460.0260.0481.0000.0830.1240.0710.0560.000
type_B0.3230.3250.1220.0580.0570.0580.0580.0220.0570.0820.0570.0580.0800.0790.0570.0590.0570.0450.0570.0560.1250.7610.0130.0000.0140.0210.0050.0000.0000.0060.0000.0000.0000.0070.0000.0090.0000.0000.0030.0050.0020.0130.0150.0100.0070.0070.0150.0000.0040.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0100.0000.0000.0050.0000.0050.0080.0220.0000.0580.0570.0820.0800.0580.0780.0570.0580.1850.0990.0590.1360.0560.1250.0831.0000.2580.2950.1180.011
type_C0.0830.0840.1500.0870.2210.0870.2260.0280.2210.3180.0850.0870.0830.1180.2230.2310.2230.2610.2220.0840.0160.6170.0140.0100.0120.0000.0120.0030.0050.0100.0000.0000.0000.0000.0060.0000.0060.0000.0000.0000.0000.0000.0060.0050.0000.0080.0160.0000.0050.0000.0030.0000.0010.0000.0000.0070.0000.0000.0050.0000.0000.0080.0040.0000.0070.0000.0240.0670.2260.2220.3180.0830.0870.0060.2210.0870.1030.0370.2310.2810.0840.0160.1240.2581.0000.4400.1760.006
type_D0.0950.0960.1730.1970.0970.0990.0990.0680.0970.1400.0970.1990.0790.0790.0980.1020.0980.0660.0970.1930.0120.8640.0000.0000.0000.0000.0130.0120.0000.0060.0050.0120.0030.0000.0000.0000.0050.0060.0000.0050.0010.0100.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0050.0000.0000.0000.0000.0000.0060.0070.0000.0050.0070.0290.0300.0990.0970.1400.0790.0990.0580.0970.1990.1380.0010.1020.0380.1930.0120.0710.2950.4401.0000.2010.009
type_E0.0370.0380.0680.0390.0380.4950.0390.2750.0380.0550.4850.0390.0540.0530.0380.0400.0390.1990.0380.0380.0690.8000.0090.0000.0000.0000.0120.0000.0090.0000.0000.0000.0030.0000.0000.0120.0000.0000.0030.0000.0020.0000.0000.0000.0040.0000.0000.0000.0000.0000.0070.0000.0020.0130.0000.0000.0000.0000.0000.0110.0000.0070.0000.0000.0000.0080.0150.0090.0390.0380.0550.0540.4950.3780.0380.0390.0550.0670.0400.2080.0380.0690.0560.1180.1760.2011.0000.000
year0.0110.0000.0000.0160.0000.0060.0000.0060.0010.0040.0000.0000.0070.0040.0000.0000.0000.0160.0000.0000.0000.0080.0130.0020.5990.0000.5610.0000.0000.0000.0050.0000.0130.0000.0050.0110.0130.0000.0000.0120.0000.0030.0000.0000.0000.0000.0000.0000.0100.0000.0000.0090.0000.0060.0000.0000.0000.0000.0000.8910.0350.0310.2000.0240.0730.1330.0440.0330.0000.0000.0040.0070.0060.0090.0010.0000.0000.0050.0000.0140.0000.0000.0000.0110.0060.0090.0001.000

Missing values

2025-03-25T14:01:40.286882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-25T14:01:40.822806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idsalesonpromotionyearmonthday_of_monthday_of_weekdays_to_next_paydaydays_after_earthquakedcoilwticoclustercity_Babahoyocity_Cayambecity_Cuencacity_Daulecity_El Carmencity_Esmeraldascity_Guarandacity_Guayaquilcity_Ibarracity_Latacungacity_Libertadcity_Lojacity_Machalacity_Mantacity_Playascity_Puyocity_Quevedocity_Quitocity_Riobambacity_Salinascity_Santo Domingostate_Bolivarstate_Chimborazostate_Cotopaxistate_El Orostate_Esmeraldasstate_Guayasstate_Imbaburastate_Lojastate_Los Riosstate_Manabistate_Pastazastate_Pichinchastate_Santa Elenastate_Santo Domingo de los Tsachilasstate_Tungurahuatype_Btype_Ctype_Dtype_Efamily_BABY CAREfamily_BEAUTYfamily_BEVERAGESfamily_BOOKSfamily_BREAD/BAKERYfamily_CELEBRATIONfamily_CLEANINGfamily_DAIRYfamily_DELIfamily_EGGSfamily_FROZEN FOODSfamily_GROCERY Ifamily_GROCERY IIfamily_HARDWAREfamily_HOME AND KITCHEN Ifamily_HOME AND KITCHEN IIfamily_HOME APPLIANCESfamily_HOME CAREfamily_LADIESWEARfamily_LAWN AND GARDENfamily_LINGERIEfamily_LIQUOR,WINE,BEERfamily_MAGAZINESfamily_MEATSfamily_PERSONAL CAREfamily_PET SUPPLIESfamily_PLAYERS AND ELECTRONICSfamily_POULTRYfamily_PREPARED FOODSfamily_PRODUCEfamily_SCHOOL AND OFFICE SUPPLIESfamily_SEAFOODis_holidayis_eventis_additionalis_transferis_bridge
02981890.00002013617013097.86100000000100000000000000000010000000000010100000000000000000000000000000000.00.00.00.00.0
11225465.0000201331065092.01140000000000000000010000000000000010000000000000000000000010000000000000000.00.00.00.00.0
216477680.00002015716315050.9020010000000000000000000000000000000000010000000000001000000000000000000000.00.00.00.00.0
38442760.000020144206100104.33140000000000000000010000000000000010000000000100000000000000000000000000000.00.00.00.00.0
421786293.00002016510152544.6840000000000010000000000000000100000000010010000000000000000000000000000000.01.00.00.00.0
5947306525.000120146171130106.9560010000000000000000000000000000000001000000000010000000000000000000000000.00.00.00.00.0
62273742172.24562016725137849.0280000000000000000010000000000000010000010000000001000000000000000000000000.00.00.00.00.0
725661793.0000201612142124351.01150000000001000000000000010000000000000100000000000000000000000000000000000.00.00.00.00.0
820829610.00002016317314040.17130000000000000100000000000000001000000010100000000000000000000000000000000.00.00.00.00.0
939756137.00002013812030106.1970000000000000000001000100000000000000100000000000100000000000000000000000.00.00.00.00.0
idsalesonpromotionyearmonthday_of_monthday_of_weekdays_to_next_paydaydays_after_earthquakedcoilwticoclustercity_Babahoyocity_Cayambecity_Cuencacity_Daulecity_El Carmencity_Esmeraldascity_Guarandacity_Guayaquilcity_Ibarracity_Latacungacity_Libertadcity_Lojacity_Machalacity_Mantacity_Playascity_Puyocity_Quevedocity_Quitocity_Riobambacity_Salinascity_Santo Domingostate_Bolivarstate_Chimborazostate_Cotopaxistate_El Orostate_Esmeraldasstate_Guayasstate_Imbaburastate_Lojastate_Los Riosstate_Manabistate_Pastazastate_Pichinchastate_Santa Elenastate_Santo Domingo de los Tsachilasstate_Tungurahuatype_Btype_Ctype_Dtype_Efamily_BABY CAREfamily_BEAUTYfamily_BEVERAGESfamily_BOOKSfamily_BREAD/BAKERYfamily_CELEBRATIONfamily_CLEANINGfamily_DAIRYfamily_DELIfamily_EGGSfamily_FROZEN FOODSfamily_GROCERY Ifamily_GROCERY IIfamily_HARDWAREfamily_HOME AND KITCHEN Ifamily_HOME AND KITCHEN IIfamily_HOME APPLIANCESfamily_HOME CAREfamily_LADIESWEARfamily_LAWN AND GARDENfamily_LINGERIEfamily_LIQUOR,WINE,BEERfamily_MAGAZINESfamily_MEATSfamily_PERSONAL CAREfamily_PET SUPPLIESfamily_PLAYERS AND ELECTRONICSfamily_POULTRYfamily_PREPARED FOODSfamily_PRODUCEfamily_SCHOOL AND OFFICE SUPPLIESfamily_SEAFOODis_holidayis_eventis_additionalis_transferis_bridge
2999921977791719.0002720165215103647.6710000000000000000000100000000000001000010000000000001000000000000000000000.00.00.00.00.0
30000437640.0000201312546095.1520010000000000000000000000000000000000010000001000000000000000000000000000.00.00.00.00.0
300011190940.000020133847092.0140000000000000000000010000000000000100010000000000000000000000000000001000.00.00.00.00.0
3000212800402057.000120141221610056.9110000000000000000000100000000000001000010001000000000000000000000000000000.00.01.00.00.0
3000367810892.63702014117414093.9630000000000000010000000000010000000000100000000000000000000000001000000000.00.00.00.00.0
3000411810583.00002014102665081.27140000000000000000010000000000000010000000000000000000000000001000000000000.00.00.00.00.0
30005269193892.490020172233531454.4890000000000000000010000000000000010000010000000000000000000000000000010000.00.00.00.00.0
300062036056124.0000201622059029.5920010000000000000000000000000000000000010000000000000000000000100000000000.00.00.00.00.0
300072319693737.453120167283310441.13110000000000000000010000000000000010000000000000000000000000000001000000000.00.00.00.00.0
3000892823716.0000201466490103.32130000000000000100000000000000001000000010000000000000100000000000000000000.00.00.00.00.0